Methods of monitoring effects of chemical agents on a sample

ABSTRACT

The invention provides methods and systems for monitoring effects of chemical agents on optical signals produced by samples in response to the chemical agents. Preferred methods comprise application of multiple chemical agents that interact to alter an optical signal from the sample. Methods and systems of the invention also comprise monitoring an optical signal from an endogenous chromophore upon application of a chemical agent to a sample. Methods and systems of the invention also comprise the use of triggers, atomizers and image alignment to enhance the results of methods described herein.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S.provisional patent application Serial No. 60/170,972, filed Dec. 15,1999, the disclosure of which application is hereby incorporated byreference.

FIELD OF THE INVENTION

[0002] This invention relates generally to spectral analysis. Moreparticularly, in one embodiment, the invention relates to determiningchemically-induced changes of optical spectra.

BACKGROUND OF THE INVENTION

[0003] Direct visual observation alone is often inadequate foridentification of abnormalities in a specimen being examined, whetherthe specimen is a biological specimen or otherwise. Observation of manymedical conditions in biological specimens of all kinds is well known.It is common in medical examination to perform visual examinations indisease diagnosis. For example, visual examination of the cervix candiscern areas where there is a “suspicion” of pathology. In someinstances, filters can be used to improve visual differentiation ofnormal and abnormal tissues. In other situations, when tissues of thecervix are examined in vivo, chemical agents such as acetic acid can beapplied to enhance the differences in appearance between normal andpathological areas. These techniques form an integral part of acolposcopic examination of the cervix. Colposcopists may amplify thedifference between normal and cancerous tissue with the application ofvarious “activation” agents, the most common being acetic acid, atapproximately 3% to 5% concentration, or an iodine solution, such asLugol's iodine or Shiller's iodine. Even when the cervical tissues areviewed through a colposcope by an experienced practitioner with theapplication of acetic acid, correct diagnosis can be affected bysubjective analysis. A variety of methods using optical techniques havebeen directed towards the diagnosis of cancer and other pathologies,particularly involving the cervix. Certain of these systems and methodshave limitations that render them unsuitable for use as screeningprocedures.

[0004] While there have been extensive developments in the field ofcancer diagnosis, none of these are well adapted for screening largepopulations. Currently, disease diagnoses are made predominately frompathological examinations of biopsied tissue. Techniques such asbiopsies, while being the definitive determination of the presence ofdisease, are labor-intensive and operator-dependent, thus unsuitable forscreening large populations. As another example, medical imagingtechniques, depending on their cost, resource requirements and patientaccessibility, may be unsuitable for population screening.

[0005] To be well accepted in the medical community, a screening methodshould be sufficiently sensitive and specific to identify abnormalitiesaccurately. Furthermore, a screening method ideally is easy to performso that it can be carried out rapidly on an otherwise healthy patient.In addition, to be cost effective the screening method should notrequire the use of expensive resources, including a significant timecommitment from costly, highly trained medical personnel. Generally,screening settings advantageously employ less skilled operators and moreoperator-independent technology.

SUMMARY OF THE INVENTION

[0006] The invention provides systems and methods for quickly andefficiently screening samples, especially biological samples. Accordingto the invention, changes in the spectral properties of tissues uponexposure to chemical agents are characteristic of the physiologicalstate of the tissue. In particular, the invention relates to changes inspectral properties of a sample in response to chemical treatment. Thesample can be a sample of tissue, and the response can be indicative ofa state of health of the tissue or the patient from whom the sample isobtained. Upon exposure to chemical agents, the light emissionproperties of a sample change. In the case of a sample of tissue, thetemporal evolution of these changes is characteristic of the state ofhealth of the tissue generally. When exposed to light, tissues emitlight having spectral properties that are characteristic of thephysiological and biochemical make-up of the tissue. When exposed to achemical agent, such as a contrast agent, the spectral properties of thetissue are changed by the interaction of the agent with endogenousmolecules in the tissue. As the chemical agent diffuses out of the areaof application, or otherwise becomes less abundant in the tissue, theemission spectrum of the tissue returns to pre-exposure levels.According to the invention, changes in tissue produced by endogenouschemical agents provide insight into the sample, such as the clinicalhealth of the tissue as described in detail below. The invention alsoinvolves systems and methods of performing the application of one ormore chemical agents, including the amount of material dispensed,dispensing patterns, and triggering a measurement relative to the timeof dispensing.

[0007] Accordingly, the invention provides methods and systems formonitoring effects of chemical agents on a sample by exposing a sampleto one or more chemical agents, and measuring a change in an opticalsignal from the sample. A preferred method of the invention comprisesdispensing a plurality of chemical agents on a sample, wherein theagents interact to alter an optical signal from the sample and measuringthe chemical agents are selected from the group consisting of aceticacid, formic acid, propionic acid, butyric acid, Lugol's iodine,Shiller's iodine, methylene blue, toluidine blue, osmotic agents, ionicagents, and indigo carmine. The chemical agents may be appliedsubstantially simultaneously, or by dispensing at least two of theplurality of chemical agents sequentially.

[0008] The invention is applicable to any sample type. Preferred methodsof the invention comprise using a biological sample. In a preferredembodiment, the sample is selected from epithelial tissue, cervicaltissue, colorectal tissue, skin, and uterine tissue.

[0009] In another aspect, a preferred embodiment of the inventionrelates to a method of monitoring effects of a chemical agent on asample comprising dispensing a chemical agent on a sample, providing anautomated triggering signal to initiate a measurement period relative tothe dispensing, and measuring an optical signal from the sample. Theautomated triggering signal can be provided prior to, substantiallysimultaneously with, or after dispensing the chemical agent. Inpreferred embodiments, the measurement is initiated at a predeterminedtime relative to the automatic triggering signal. In yet another aspect,methods of the invention comprise of diagnosing the state of health of aapplying the chemical agent or agents as a mist onto the sample.

[0010] In a preferred embodiment, the predefined pattern issubstantially circular. In another preferred embodiment, the predefinedpattern is substantially annular.

[0011] In preferred embodiments, the chemical agent is dispensed at acontrolled rate, or a controlled volume of the chemical agent isdispensed, or both.

[0012] In a still further aspect, the invention comprises dispensing achemical agent on a sample, capturing a plurality of sequential imagesof the sample during a measurement period, automatically aligning asubset of the plurality of images to spatially correlate the subset ofimages, measuring an optical signal from the subset of the spatiallycorrelated images, and providing a diagnosis of a state of health of thesample based at least in part on the optical signal.

[0013] In a preferred embodiment, aligning further comprises aligningthe subset to compensate for relative motion between the sample and aspectral observation device. In another preferred embodiment, aligningfurther comprises aligning the subset to compensate for relative motionbetween a first portion of the sample and a second portion of thesample.

[0014] In a still further aspect, the invention provides methods fordetermining a tissue response in which a chemical agent is applied to atissue and an optical property of an endogenous molecule in the tissueis measured. In a preferred embodiment, the endogenous molecule is achromophore, for example a fluorophore. Method of the invention compriseapplying the chemical agent and monitoring an optical signal from theendogenous molecule. The presence, absence, or change in the signal maybe indicative of disease when compared to known standards. Suchstandards may be empirically derived or may be obtained from the art.The endogenous chromophore is preferably hemoglobin, a porphoryin, NADH,a flavin, elastin, or collagen.

[0015] In preferred methods, the optical signal is a light signal, suchas a fluorescent or white light spectrum. The optical signal may also bea spectrum produced, at least in part by light-scattering properties ofthe tissue.

[0016] Also in preferred methods, the optical signal may be a decayfunction. The optical signal is compared to a standard responseassociated with healthy or diseased tissue, including tissue at variousstages of disease. Such standards may be determined empirically or knownin the art. Alteration of an optical signa alone may be indicative ofthe health of the patient from whom a sample was obtained.

[0017] The foregoing and other objects, aspects, features, andadvantages of the invention will become more apparent from the followingdescription and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The objects and features of the invention can be betterunderstood with reference to the drawings described below, and theclaims. The drawings are not necessarily to scale, emphasis insteadgenerally being placed upon illustrating the principles of theinvention. In the drawings, like numerals are used to indicate likeparts throughout the various views.

[0019]FIG. 1 shows an exemplary spectroscopic system that employs aplurality of spectral types according to an illustrative embodiment ofthe invention;

[0020]FIG. 2 shows an exemplary operational block diagram of thespectroscopic system of FIG. 1;

[0021]FIG. 3 is a detailed schematic flow diagram showing exemplarysteps of combining a fluorescence spectrum analysis with a reflectancespectrum analysis according to an illustrative embodiment of theinvention;

[0022]FIG. 4 is a schematic diagram of another illustrative systemuseful for monitoring the effects of a chemical agent on a specimen, andwhich embodies principles of the invention;

[0023]FIG. 5 is a graph that shows trend lines of data observedaccording to principles of the invention;

[0024] FIGS. 6A-6C are diagrams that show raw data observed from variousspecimens, according to principles of the invention;.

[0025]FIG. 7 is a graph showing curves representing averages of dataprocessed according to principles of the invention;

[0026]FIG. 8 is a graph plotting computed ratios that show a basis fordifferentiating CIN II/III lesionf from CIN I and normal tissue forindividual specimens, according to principles of the invention;

[0027]FIG. 9 is a graph showing responses, normalized at 480 nm, fromtissues as function of wavelength, according to principles of theinvention;

[0028]FIG. 10 is a functional block diagram of an embodiment of aanother illustrative system useful for monitoring the effects of achemical agent on a specimen according to the invention;

[0029]FIG. 11 is a functional block diagram of an embodiment of anillustrative hand-held system useful for monitoring the effects of achemical agent on a specimen according to the invention;

[0030] FIGS. 12A-12C depict schematic arrangements for illustrativefilter wheels useful in the system of FIG. 11;

[0031]FIG. 13 is a functional block diagram of an embodiment of a systemuseful for monitoring the effects of a chemical agent on a specimenaccording to the invention;

[0032]FIG. 14 is a schematic diagram of a filter wheel useful in thesystem of FIG. 13;

[0033]FIG. 15 is a functional block diagram of an embodiment of a systemuseful for monitoring the effects of a chemical agent on a specimenaccording to the invention;

[0034]FIG. 16 shows a schematic diagram of a CCD device for use in thesystem of FIG. 15;

[0035]FIG. 17 is a graph showing the time dependence of backscatteredresponses at 600 nm for various tissue classes (NED, CIN II and CINIII), recorded using systems and methods of the invention;

[0036] FIGS. 18A-18C are diagrams depicting various aspects of a mucosalatomizer device used to spray a chemical agent uniformly onto thesurface of a specimen, and to provide a trigger mechanism useful forinitiating an optical observation, according to principles of theinvention;

[0037]FIG. 19 is a graph of absorption spectra recorded for NADH using achemical agent according to the invention;

[0038]FIG. 20 is a graph of fluorescence spectra as a function of timefor specimens treated with a chemical agent according to principles ofthe invention;

[0039]FIG. 21 is a graph of flourescence spectra recorded before andafter treatment of a specimen with a chemical agent, according toprinciples of the invention;

[0040]FIG. 22 is a one-dimensional diagram of watershed segmentation;

[0041]FIG. 23 is a graph of a signal and its first derivative; and

[0042]FIG. 24 shows a sigmoidal scaling function used to enhance thecontrast between light and dark regions of an image.

DETAILED DESCRIPTION

[0043] Acetowhitening of cervical tissue has long been known to be aqualitative aid to locating lesions during colposcopic examination.However, accurate quantitative measurements of acetowhitening ofcervical epithelial tissue, as a function of time and wavelength, havenot been reported. Quantitative analysis of the acetowhitening processcan significantly increase the sensitivity and specificity oftraditional colposcopy.

[0044] The invention will be described in terms of multiple embodimentsthat relate to the observation of chemically-induced changes in opticalspectra, particularly in the area of medical diagnostics, and especiallyas it relates to the analysis of spectra obtained from human cervicaltissue in the detection of cervical cancer. However, the invention hasapplicability generally in the area of chemically-induced changes inoptical spectra.

[0045]FIG. 1 depicts an exemplary spectroscopic system 100 employing aplurality of spectral data types in methods and systems according to anillustrative embodiment of the invention. The spectroscopic systemincludes a console 102 connected to a probe 104 by a cable 106. Thecable 106 carries electrical and optical signals between the console 102and the probe 104. The probe 104 accommodates a disposable component 108which is used only once, and discarded after use. The console 102 andthe probe 104 are mechanically connected by an articulating arm 110,which can also support the cable 106. The console 102 contains much ofthe hardware and the software of the system, and the probe 104 containsthe necessary hardware for making suitable spectroscopic observations.The details of the system are further explained in conjunction with FIG.2.

[0046]FIG. 2 shows an exemplary operational block diagram 200 of aspectroscopic system of the type depicted in FIG. 1. According to anillustrative embodiment, the spectroscopic system of FIGS. 1 and 2 issubtantially the same as single-beam spectrometer devices, but isadapted to include the features of the invention. The console 102includes a computer 202 which executes software that controls theoperation of the spectroscopic system 100. The software includes one ormore modules recorded on machine-readable media, which can be any mediumsuch as magnetic disks, magnetic tape, CD-ROM, semiconductor memory, orthe like. Preferably, the machine-readable medium is resident within thecomputer 202. In alternative embodiments, the machine-readable mediumcan be connected to the computer 202 by a communication link. Inalternative embodiments, one can substitute computer insructions in theform of hardwired logic for software, or one can substitute firmware(i.e., computer instructions recorded on devices such as PROMs, EPROMSoe EEPROMs, or the like) for software. The term machine-readableinstructions as used herein is intended to encompass software, hardwiredlogic, firmware and the like.

[0047] The computer 202 is a general purpose computer. The computer 202can be an embedded computer, or a personal computer such as a laptop ordesktop computer, that is capable of running the software, issuingsuitable control commands, and recording information in real time. Thecomputer 202 has a display 204 for reporting information to an operatorof the spectroscopic system 100, a keyboard 206 for enabling theoperator to enter information and commands, and a printer 208 forproviding a print-out, or permanent record, of measurements made by thespectroscopic system 100 and for printing diagnostic results, forexample, for inclusion in the chart of a patient. As described below inmore detail, in an illustrative embodiment of the invention, somecommands entered at the keyboard, enable a user to select a particularspectrum for analysis or to reject a spectrum, and to select particularsegments of a spectrum for normalization. Other commands enable a userto select the wavelength range for each particular segment and tospecify both wavelength contiguous and non-contiguous segments.

[0048] The console 102 also includes an ultraviolet (UV) source 210 suchas a nitrogen laser or a frequency-tripled Nd:YAG laser, a white lightsource 212 such as one or more Xenon flash lamps, and controlelectronics 214 for controlling the light sources both as to intensityand as to the time of onset of operation and the duration of operation.One or more power supplies 216 are included in the console 102, toprovide regulated power for the operation of all of the components. Theconsole 102 also includes at least one spectrometer and at least onedetector (spectrometer and detector 218) suitable for use with each ofthe light sources. In some embodiments, a single spectrometer canoperate with both the UV light source and the white light source. Insome embodiments, the same detector can record UV and white lightsignals, and in some embodiments different detectors are used for eachlight source.

[0049] The console 102 also includes coupling optics 220 to couple theUV illumination from the UV light source 210 to one or more opticalfibers in the cable 106 for transmission to the probe 104, and forcoupling the white light illumination from the white light source 212 toone or more optical fibers in the cable 106 for transmission to theprobe 104. The console 102 also includes coupling optics 222 to couplethe spectral response of a specimen to UV illumination from the UV lightsource 210 observed by the probe 104 and carried by one or more opticalfibers in the cable 106 for transmission to the spectrometer anddetector 218, and for coupling the spectral response of a specimen tothe white light illumination from the white light source 212 observed bythe probe 104 and carried by one or more optical fibers in the cable 106for transmission to the spectrometer and detector 218. The console 102includes a footswitch 224 to enable an operator of the spectroscopicsystem I 00 to signal when it is appropriate to commence a spectralobservation by stepping on the switch. In this manner, the operator hashis or her hands free to perform other tasks, for example, aligning theprobe 104.

[0050] The console 102 includes a calibration port 226 for calibratingthe optical components of the spectrometer system. The operator placesthe probe 104 in registry with the calibration port 226 and issues acommand that starts the calibration operation. In the calibrationoperation, a calibrated light source provides illumination of knownintensity as a function of wavelength as a calibration signal. The probe104 detects the calibration signal. The probe 104 transmits the detectedsignal through the optical fiber in the cable 106, through the couplingoptics 222 to the spectrometer and detector 218. A test spectral resultis obtained. A calibration of the spectral system is computed as theratio of the amplitude of the known illumination at a particularwavelength divided by the test spectral result at the same wavelength.

[0051] The probe 104 includes probe optics 230 for illuminating aspecimen to be analyzed with UV and white light from the UV source 210and the white light source 212, and for collecting the fluorescent andbackscatter (or reflectance) illumination from the specimen that isbeing analyzed. The probe includes a scanner assembly 232 that providesillumination from the UV source 210 in a raster pattern over a targetarea of the specimen of cervical tissue to be analyzed. The probeincludes a video camera 234 for observing and recording visual images ofthe specimen under analysis. The probe 104 includes a targeting souce236, which can be used to determine where on the surface of the specimento be analyzed the probe 104 is pointing. The probe 104 also includes awhite light illuminator 238 to assist the operator in visualizing thespecimen to be analyzed. Once the operator aligns the spectroscopicsystem and depresses the footswitch 224, the computer 202 controls theactions of the light sources 210, 212, the coupling optics 220, thetransmission of light signals and electrical signals through the cable106, the operation of the probe optics 230 and the scanner assembly 232,the retreival of observed spectra via the cable 106, the coupling of theobserved spectra via the coupling optics 222 into the spectrometer anddetector 218, the operation of the spectrometer and detector 218, andthe subsequent signal procesing and analysis of the recorded spectra.

[0052]FIG. 3 is a detailed schematic flow diagram 300 showing exemplarysteps of combining fluorescence spectrum analysis with reflectancespectrum analysis to perform tissue characterization according to anillustrative embodiment of the invention. Step 310 indicates thatfluorescence spectra from a test specimen of unknown condition orunknown state of health are available. At step 320, the computer 202determines whether the test specimen can be classified as “normal,” or“metaplasia,” or can not be classified by fluorescence spectroscopyalone. As indicated in step 325, a decision is taken as to whether thetest specimen has a definitive state of health, for example that thespecimen is “normal.” If the test specimen can be classified, forexample as normal, the method ends at step 330.

[0053] In the event that a definitive condition or state of healthcannot be ascribed to a test specimen, the computer 202 further analysesinformation available from a reflectance spectrum or from a plurality ofreflectance spectra taken from the test specimen. At step 335, thecomputer 202 provides processed reflectance spectra.

[0054] If the specimen cannot be classified, a mean normalization stepis performed by computer 202, as indicated at step 340. The meannormalization is carried out using a plurality of reflectance spectrataken from specimens that are known to represent normal squamous tissue.In one embodiment, a single test specimen is examined at multiplelocations, each location measuring approximately one millimeter indiameter. If one or more locations of the test specimen providefluorescence spectra that indicate that those locations can beclassified as representing normal squamous tissue, the reflectancespectra recorded from those locations are used to mean normalize thereflectance spectra obtained from locations that are not capable ofbeing classified as “normal” or “metaplasia” solely on the basis offluorescence spectra.

[0055] As indicated in step 350, the computer 202 can carry out ananalysis using a metric, for example using the Mahalanobis distance as ametric in N-dimensional space. In one embodiment, the test reflectancespectra are truncated to the wavelength regions 391 nm to 484 nm, and532 nm to 625 nm. In one embodiment, the classifications CIN I and CINII/II are the classifications that are possible for a test spectrum thatis neither classified as “normal” nor “metaplasia” by fluorescencespectral analysis. As indicated at step 350, the computer 202 classifiesthe test specimen as having a condition or state of health selected fromCIN I and CIN II/III based on the value of the metric computed by thecomputer 202, provided that the value of the metric does not exceed apre-determined maximum value.

[0056] At step 360, the computer 202 presents the results of theclassification of the test specimen, as a condition or state of healthcorresponding to one of normal, metaplasia, CIN I and CIN II/III.

[0057]FIG. 4 shows a schematic diagram of an illustrative system 600embodying principles of the invention. A standard colposcope 610 (Zeiss,Model 1-FC ZMS-506-II) is modified by adding video image capturecapability with permanent and electronic storage of data to allowcapturing of time-sequenced images during a routine colposcopicexamination. The colposcope 610 has magnification capabilities of 4×,6×, 10×, 16×, and 25×, and is illuminated by a fiber optic-coupled 12volt/100 watt halogen lamp 620 with 20× eye binoculars. A three-channelcharge-coupled device color video camera (DAGE-MTI, Model DC-330) 630 ismounted to the colposcope 610. The computer 650 includes an integratedvideo frame-grab board and video display card at 24-bit resolution forcapturing images. Images can be captured at a rate of at least about oneimage per second. The computer 650 also includes image control software(TeleComputing Solutions, ColpoShot™) that interfaces with the videoframe grab board for archiving images, for example into patients'medical records. The ColpoShot™ software is modified to allow forintensity measurements at specific sites as a function of time andwavelength (as resolved by four discrete filters in a filter wheel 640,described in more detail below). The computer 650 also includes controlsoftware for controlling the change of filters in the high-speed filterwheel 640. This software is synchronized with the data collection (imagecapture) software so each image is associated with a spectral regioncorresponding to a particular filter. Time-stamping of each image isperformed so each image can be placed in proper time sequence.

[0058] In the illustrative system 600, the filter wheel 640 is from aLudl Electronics Ltd., with an RS 232 and GPIB 488 computer interfacefor resolving optical signals with respect to wavelength. Images aremeasured and recorded at three separate wavelength bands in the visiblespectral region. The first wavelength band is near 400 nm, with abandwidth of about 20 nm to about 30 nm. The second wavelength band isnear 525 nm with a bandwidth of about 30 nm. The third wavelength bandis near 680 nm with a nominal bandwidth of about 30 nm. In addition tothe images taken through the filter wheel 640, a fourth image usingunfiltered illumination is taken as part of the data set. The unfilteredimages allow data analysis of red (R), green (G), and blue (B)components for comparison with filtered image data. As described before,crossed polarizers mounted in the optical path, one associated with thelight coming from the illumination source 620, and one associated withthe light from the image to be observed and recorded, are used to reduceunwanted glare from the surface of the cervix.

[0059] The illustrative system 600 is controlled by the computer 650,having capabilities similar to the computer 140 described earlier. Thecomputer 650 has associated with it software to operate the computer650, to provide input and output interactions with an instrument user,to control and synchronize the various components of the illustrativesystem 600, and to record, analyze, and report data obtained from theillustrative system 600.

[0060] The illustrative system 600 is configured to capturetime-separated images of the specimen during routine colposcopicexaminations. Digital images are recorded at a 4 x magnification givinga panoramic view of the entire cervical field at maximal aceticwhitening. In the illustrative embodiment, images are taken about everysecond for about 5 minutes after the application of acetic acid. Thecomputer 650 rotates the filter wheel 640 to allow for imaging atdifferent wavelengths.

[0061] In operation, an illustrative embodiment of the process ofobtaining images is as follows. The first image following theapplication of the acetic acid is an unfiltered image. Next, the filterwheel 640 is rotated to bring the short-wavelength (˜400 nm) filter intoplace and the next image is recorded. Then, the ˜525 nm filter ispositioned, and the next image is recorded. Next, the long-wavelength(˜680 nm) filter is positioned and the last image of the sequence isrecorded. This process takes four seconds to complete. After this firstcycle through the filter wheel 640, the process repeats with anotherunfiltered image, followed by the sequence of filtered images. Theprocess of observing and recording images continues without stopping fora duration of 300 seconds. The resulting data are seventy-fiveunfiltered images of the evolution of an optical signal from a specimentreated with a chemical agent, such as cervical acetowhitening, and atotal of seventy-five images in each of the three filtered spectralregions. As will be appreciated by those of skill in the spectroscopicarts, the precise sequence of observing and recording images in thevarious wavelength bands depends on the sequence of placement of filterswithin the filter wheel 640 and the sense of rotation of the wheel 640.Alternative sequences of observation can be employed with substantiallyequivalent results. The duration of operation can be shortened orextended from the illustrative 300 seconds just described depending onthe situation, which can be influenced by the kind of specimen and howit is to be examined (e.g., specimen characteristics, such as cervix,larynx, skin, and the like, specimen in vivo or in vitro, use ofdifferent chemical agents, the disease conditions to be investigated,and the like).

[0062] Illustratively, time-stamped images are saved to disk at 20second intervals. In one embodiment, treatment of a specimen with achemical agent is accomplished as follows. A solution of 5% acetic acidis applied with solution-soaked cotton balls placed in contact with thesurface of the cervix for about 15 seconds. An alternative method ofapplication of a chemical agent is discussed below. In one embodiment,the time sequence image capturing software is run immediately before theapplication of acetic acid, to obtain baseline measurements.

[0063] In one embodiment, the parameters that are extracted from theobservations include the rate of acetowbitening, the maximum intensityof the whitening, and the final rate of decay of the whitening. Once thedata is collected, the images are analyzed by the computer 650 withsoftware that calculates four parameters (mean Luminance, and mean red(R ), green (G), and blue (B) intensities) within user-defined Regionsof Interest (ROI's). The software enables the user to mark, with a mousecontrolled cross-hair cursor, 5 pixel by 5 pixel ROI's on a location inan image. A biopsy can subsequently be taken by the colposcopist, topermit a comparison of the results obtained from the methods of theinvention with the results of the biopsy. Once ROI's have been manuallymarked on all images in the timed-sequence, mean Luminance and mean R,G, B intensities within the 5 pixel by 5 pixel ROI's are calculated andoutput in tabular form. Also included in the output recorded in thetable are the following data elements; image number, ROI location,elapsed time in seconds, and the standard deviation and median of theLuminance and R, G, B values. In one embodiment, the ratio of the meangreen intensity to the mean red intensity is found to yield accurateresults.

[0064] In this embodiment, to calibrate the utility of the system andmethod, five (5) biopsy-confirmed CIN II/III lesions are measured, five(5) biopsy-confirmed CIN I lesions are measured, five (5)colposcopy-confirmed normal mature squamous tissue regions are measuredand one (1) biopsy-confirmed normal mature squamous tissue region ismeasured. Data are analyzed by graphing the Green intensity divided bythe Red intensity and normalizing by the maximum intensity within eachpatient.

[0065]FIG. 5 is a diagram 700 that shows the trend lines of ROIscorrelated to CIN II/III lesions (curve 706), CIN I lesions (curve 708),and normal mature squamous tissue (curve 710). The trend lines areplotted using the ratio of mean green intensity to mean red intensity,normalized to maximum intensity, as the vertical axis 702 (expressed inarbitrary units), and using the time after application of acetic acid tothe tissue, expressed in seconds, as the horizontal axis 704.

[0066] FIGS. 6A-6C are diagrams, generally 800, that show graphs of rawdata plotted using the ratio of mean green intensity to mean redintensity, normalized to maximum intensity, as the vertical axis 802(expressed in arbitrary units), and using the time after appllication ofacetic acid to the tissue, expressed in seconds, as the horizontal axis804. FIG. 6A is a diagram that shows the raw data of ROIs correlated toCIN II/III lesions, as curves 810, 812, 814, 816, 818 representingobservations taken from five individuals. FIG. 6B is a diagram thatshows the raw data of ROIs correlated to CIN I lesions, as curves 820,822, 824, 826, 828 representing observations taken from fiveindividuals. FIG. 6C is a diagram that shows the raw data of ROIscorrelated to normal mature squamous tissue, as curves 830, 832, 834,836, 838 representing observations taken from five individuals.

[0067] An operator of the illustrative system and method defines aregion of interest on an image. The intensity readings of the pixels inthis region are averaged to provide a quantitative value of brightnessas recorded through the particular filter (or unfiltered). By plottingthese values as functions of time, a picture of the evolution of theacetowhitening at the selected location in the image is created.

[0068] A clinically useful tool based on the acetowhitening kineticcharacteristics analyzes the data to differentiate CIN II/III lesionsfrom CIN I lesions and normal mature squamous tissue. According to oneillustrative embodiment, the technique uses mean values from 100 secondsegments of individual patient kinetic curves. The curves are processedby calculating the mean of segments along the curve, i.e. the mean valueof the data in the temporal range from about 100 -200 seconds afterapplication of the chemical agent, the mean value of the data in thetemporal range from about 200-300 seconds after application of thechemical agent, and so forth. FIG. 7 is a graph 900 showing curves ofthe averages of data processed in this manner, in which the averagevalues are plotted along the vertical axis 902 (expressed in normalizedunits), and using the time after application of acetic acid to thetissue, expressed in seconds, as the horizontal axis 904. The curve 906represents data relating to CIN II/III lesions. The curve 908 representsdata relating to CIN I lesions. The curve 910 represents data relatingto mormal mature squamous tissue.

[0069]FIG. 8 is a scatter plot 1000 generated by taking the ratio ofmean values from two time intervals (100-200 seconds) and (200-300seconds) for data from individual specimens. In FIG. 8, the averagevalues for the time interval 200 seconds to 300 seconds (expressed innormalized units) are plotted along the vertical axis 1002, and the timeinterval 100 seconds to 200 seconds (expressed in normalized units), isplotted as the horizontal axis 1004. The points 1006 represent datarelating to CIN II/III lesions. The points 1008 represent data relatingto CIN I lesions. The points 1010 represent data relating to mormalmature squamous tissue. FIG. 8 shows a basis for differentiating CINII/III for individual specimens. An illustrative line 1020 is a line ofdemarcation between the CIN II/III data and the remaining data. A secondtechnique using the first derivative of the curves shown in FIG. 7 isalso operative . This technique yields similar results to those shown inFIG. 8.

[0070] According to another illustrative embodiment of the invention, anindication of the presence or lack of cancerous or precancerous tissueis obtained by recording the optical response in two parts of thevisible spectrum. In this embodiment, the inventors have observed thatat short wavelengths, such as 380 nm, absorption by hemoglobin canreduce signal intensities. Optical responses are recorded in that partof the spectrum where optical response variation can be detected due tomorphological changes in tissue which are associated with cancerous andprecancerous tissue, such morphological variations having a strongimpact on light scattering. At longer wavelengths, beyond 590 nm and toabout 750 nm, scattering of light from cancerous tissue wassubstantially greater than from normal tissue, and thus the reflectedresponses from cancerous tissue in that spectral range were greater thanfrom normal tissue.

[0071] It is desirable to standardize the responses from the tissueusing a signal at a wavelength where both of these influences arerelatively weak. In one embodiment, the system of the inventionstandardizes responses at 480 nm for this purpose. In one embodiment,the response, e.g., the observed reflectance, is recorded at threewavelengths, and the responses obtained at the short wavelength (between360 and 440 nm) and at the long wavelength (between 590 and 750 nm) aredivided by the response at 480 nm. According to one illustrativemethodology of the invention, normalized reflections at longerwavelengths indicate cancerous and precancerous tissue, while lowerintensity normalized refelections indicate healthy tissue. According toa further illustrative methodology of the invention, reflections in theshort wavelength part of the spectrum indicate cancerous andprecancerous tissue, while higher intensity reflections indicate healthytissue.

[0072] An algorithm using the rate of change of white light reflectionat some specific wavelength, for instance, at 600 nm, can provideaccurate differentiation between pathologic and healthy tissue withinthe first 60 seconds after the application of a pathologydifferentiating agent like acetic acid. Other algorithms, using both theaforementioned rate of change, or the time lapsed to reach maximum backscattering after application of a differentiating agent, or the timerequired to attain specific back scattered (normalized) thresholdvalues, permit the diagnosis of the presence or absence of cancer in thescreened cervix.

[0073] As an aspect of the invention, methods are provided that employspecific algorithms to analyze the back-scattered responses obtained atthe preselected wavelength or wavelengths either with or without achemical agent. Algorithms further provide for classifying examinedtissues as normal or pathological. In certain embodiments, these systemsare characterized by ease of operation, simplicity and ruggedness.

[0074]FIG. 9 presents a graph 1100 showing two data curves 1102, 1104obtained from healthy (no evidence of disease, or NED) and cancerous(CIN) tissue respectively. Normalized intensity is plotted along thevertical axis 1106 and wavelength (in units of nm) is plotted along thehorizontal axis 1108. All received responses (I_(λ)) are normalized bydividing the intensities received by the intensity obtained at anarbitrary wavelength. Reflections measured at 480 nm are used for thispurpose, since it is in a part of the spectrum where the responses'intensities are relatively independent of the tissue state (healthy orpathological). FIG. 9 shows that the received intensities at longerwavelength (between 550 to 750 nm) are consistently higher for canceroustissue (curve 1104) than for healthy tissue (curve 1102). The dataindicate that the use of three wavelengths from the reflected spectrumof tissue provides correlation between the presence or absence of cancerin the target tissue.

[0075] In one embodiment, an algorithm utilizes the reflected readingfrom the tissue at the three selected wavelengths to produce anindicator of the presence or absence of a pathology in the targettissue, or to create an artificial pathology image of the tissueobserved. In the first step of the algorithm, the responses arecollected at three wavelengths for each point observed. In oneembodiment, the following three wavelengths can be used:

[0076] λ₁=380 nm

[0077] λ₂=480 nm

[0078] λ₃=650 nm

[0079] It is understood that one can select wavelength ranges ratherthan specific narrow bands as illustrated here. Normalized reflectedintensities may then be defined:

[0080] R₃₈₀=I(λ₁)/I(λ₂)

[0081] R₆₅₀=I(λ₃)/I(λ₂)

[0082] where I(λ₁), I(λ₂) and I(λ₃) are the measured reflectedintensities at λ₁, λ₂ and λ₃ respectively. These normalized intensitiesR₃₈₀ and R₆₅₀ (which are dimensionless), can vary from about 0.2 toabout 6. In one embodiment, the intensity of the reflected light at 380and 650 nm are normalized, where the normalization parameter is thereflected intensity at 480 nm. It should be evident to those of ordinaryskill in the art that while in one embodiment R₃₈₀ is defined at λ=380nm and R₆₅₀ at λ=650 nm, one can define R(low λ) and R(high λ) aroundneighboring wavelengths in the respective ranges as well, using datasuch as presented in FIG. 9 from a number of subjects and tissue withvarying pathologies in those subjects as a “training set” to calibratethe apparatus being employed. The selection of the “bandwidth” aroundthe center wavelength is related to the kind of instrumentation selectedfor the actual device, as described below in more detail.

[0083] As long as the bandwidths selected during the calibration ortraining of the device and its subsequent use in the field for screeningpurposes are the same, good correlation is found between high values ofR₆₅₀ coupled with low values of R₃₈₀ and the presence of cancerous andprecancerous, or CIN, tissue. Similarly, good correlation is foundbetween low values of R₆₅₀ and high values of R₃₈₀ and the presence ofhealthy, or NED, tissue. Specifically, for cervical tissue that whenR₆₅₀<3.1 and R₃₈₀>1.1, the tissue is healthy (NED) and when R₆₅₀>3 andR₃₈₀<0.9 the tissue is cancerous or precancerous (CIN of all grades).

[0084] In one embodiment, a grading algorithm is incorporated in a dataprocessing unit employed by these systems and methods. The gradingalgorithm utilizes the pair (R₆₅₀, R₃₈₀) and classifies the reflectionsfrom each site observed into three groups. In the case of cervicaltissue, the algorithm classifies reflections for which the pair obeysR₆₅₀<2.9, R₃₈₀>0.1.1 as “healthy tissue” or NED. Similarly, a secondgroup of sites, for which the pair obeys R₆₅₀>3.5, R₃₈₀<0.9 isclassified as cancerous or precancerous tissue or CIN. Finally, a thirdgroup of tissue, including those tissues for which the reflections pairsobey the relationships 2.9<R₆₅₀<3.5, 0.9<R₃₈₀<0.1.1, is classified astissue for which a determination cannot be made. An algorithm accordingto these systems and methods classifies each point in the observedtissue as healthy or unhealthy. If this classification can not beperformed for a particular tissue area, that area is segregated into athird, “unclassifiable” class.

[0085] An algorithm according to these systems and methods maps tissuefor the presence or absence of a pathology. In one embodiment, analgorithm utilizes an independently determined set of threshold valuesfor R₃₈₀ and R₆₅₀. These threshold values are determined in clinicalstudies from a large number of patients from which both readings of R₃₈₀and R₆₅₀ are compared with biopsies taken from the tissues from whichthese values are determined. The threshold values as well as the actualwavelengths where the reflections are taken (and the normalizingwavelength utilized to determine from I(λ) the normalized reflectionR_(λ)) can vary from the values presented herein, as long as the shortwavelengths reflections correlate well with absorption by hemoglobin andthe long wavelengths reflections with variations of scattering betweenhealthy and pathological tissues.

[0086] The wavelengths presented in the example above and shown in FIG.9 are useful in the diagnosis of cervical tissue abnormalities. It isunderstood, however, that other wavelengths may be useful, particularlywhen other tissue areas are studied. Furthermore, the critical thresholdvalues of the short and long wavelengths standardized reflections, R,are subject to determination for each type of tissue targeted.

[0087] In another embodiment of the invention, a tissue integralalgorithm is used, where the cervix as a whole is examined to determineif a pathology exists without actually obtaining an image of thelocation of such pathology within the tissue. This algorithm is used asfollows. The computer 650 collects the normalized reflection R₆₅₀ forall measured sites on the tissue and determine the minimum R₆₅₀(min) ofthe set {R₆₅₀}. The computer 650 determines the maximum value R₆₅₀(max)of the set {R₆₅₀}. In one embodiment, if the conditionR₆₅₀(max)<1.2R₆₅₀(min) of the set {R₆₅₀} is true (e.g., if all observedvalues of R₆₅₀ are smaller than 120% of the smallest value of R₆₅₀R₆₅₀(min)), then the tissue is free of pathology. If this condition isnot met, pathology of some type is indicated, and the subject should bereferred for additional diagnostic tests to identify the type andlocation of the suspected cervical pathology.

[0088] A similar algorithm involving R₃₈₀ can be used, whereby thecomputer 650 determines the minimum R₃₈₀(min) of the set {R₃₈₀}, for thenormalized reflection R₃₈₀ observed for all tissue locations. Thecomputer 650 determines the maximum value R₃₈₀(max) of the set {R₃₈₀).In one embodiment, if the condition R₃₈₀(max)<1.20R₃₈₀(min) of the set{R₃₈₀} is true, (e.g., if all observed values of R₃₈₀ are smaller than120% of the smallest value of R₃₈₀, R₃₈₀(min)), then the tissue is freeof pathology. If this condition is not met, pathology of some type isindicated, and the subject should be referred for additional diagnostictests to identify the type and location of the suspected cervicalpathology.

[0089] It is understood that an algorithm in which both of the aboveconditions are met also results in a valid classification of the subjectpopulation into healthy and possibly pathological tissue. It shouldfurther be clear that an algorithm based on simultaneously satisfyingboth conditions can be a useful grading system of tissue for thepresence or lack of pathology. Such an algorithm can be expected toresult in a greater number of “undetermined” cases. However, theconfidence level of correctly grading healthy and pathologic tissue ishigher that when using either one of the tissue integral algorithmsdescribed above individually.

[0090] It should furthermore be evident to those of ordinary skill inthese arts that other algorithms can be constructed without departingfrom the scope of the systems and methods described above but thatnonetheless rely upon the fact that scattering from non-pathologicaltissue at wavelengths between about 600 mn and about 750 nm isconsistently greater for pathological tissue than for healthy tissue, orthat rely upon the fact that absorption of light in the range of about370 nm to about 430 nm is greater for pathological tissue than forhealthy tissue. Such algorithms, consistent with these systems andmethods, are useful in classifying a subject's cervix for the presenceor lack thereof of pathological tissue (e.g., a state of health of asubject's cervix). In other embodiments, algorithms can employ datacollected at other wavelengths in order to diagnose pathologies of thecervix or pathologies of other body tissues.

[0091]FIG. 10 shows an illustrative embodiment of a device fordetermining the presence or absence of pathology in a tissue of thecervix according to the invention. In this figure, a screening device(shown generally at 1200) is an integral part of a colposcope 1202, andis used to make determinations of tissue pathology point by point. Acolposcope 1202 is provided with a high intensity light source andoptics to view cervical tissue, all included within the colposcope 1202.The image viewed by an observer 1203 is recorded with a video camera1210 and recorded for future reference on magnetic media through a videotape recorder 1211. The instrument depicted in FIG. 10 is used fordetermining the presence or absence of pathology point by point. Sinceit is paired with a colposcope 1202, this embodiment is suitable for useby highly trained professionals, such as gynecologists. A beam splitter1212 is used to select a site in the target tissue 1213 which isilluminated with white light 1214 from the light source provided in thecolposcope 1202. The position control of the beam splitter (and thusselection of the point examined in the target tissue) is accomplishedwith a “joystick” 1215. The optical head 1216 includes a small laserdiode (wavelength at about 635 nm) 1217, having a beam coaxial with theoptical head's detection optics. In operation, the red beam is pointedtoward the tissue 1213. Since the optical axis of the laser diode 1217and the collection optics in the optical head 1216 are the same, theoptical head 1216 measures the light reflected from the pointilluminated by the red laser diode beam. In order to maintain thecalibration of the optical head's sensor 1218, a white reflector 1219 isprovided within the illumination path of the colposcope, to which theoperator directs the seeking beam from the laser diode 1217. Thereflectance from the white reflector 1219 is used as a standard forcalibrating the sensor 1218. Such a white reflector can be made fromSpectrolon™, from Labsphere Corporation. Alternatively, high purityBaSO₄ reflecting paint from the Kodak Corporation can be applied to aflat surface and used.

[0092] In some embodiments of the invention, a polarizer is interposedin the back scattered beams which considerably reduces the specularreflection from the target tissues. The specular reflection isunderstood to comprise the light reflected from the thin film ofmoisture overlaying the target tissue that has not interacted with theunderlying tissue.

[0093] In operation, the physician directs the beam 1214 to a specificsite on the suspected tissue 1213. The reflected light from this site iscollected by the optical head 1216. A spectrometer 1220 (which can beeither a refractive or dispersive spectrometer) disperses the light sothat the intensity of the reflected light at preselected wave lengthscan be measured in the detector 1218. In one illustrative embodiment,three preselected wavelengths are chosen. In certain embodiments, thesensor 1218 comprises a plurality of sensors corresponding in number tothe number of preselected wavelengths, so that one sensor is dedicatedto each wavelength. The sensor 1218 can be an ICCD, a standard CCD, orany other detector system known in the art or envisioned by those ofordinary skill in these arts.

[0094] Data from the sensor 1218 is analyzed in a computer processor1221 by applying an algorithm system as described above, and a score isobtained from the data processing that relates to the presence orabsence of pathology at the tissue area being illuminated by the laserdiode 1217. This score is graphically represented on a display 1222. Thedigital information corresponding to the score is made availableelectronically for further processing or representation. In certainembodiments, points for which pathological scores are obtained can berepresented on a display 1222 as superimposed upon an image provided bya video camera 1210. In one embodiment, abnormal points are identifiedgraphically with an artificial color not commonly found in cervicaltissue, such as shades of green. It will be seen below that otherembodiments provide for creation of artificial images or representationsof pathologies. The embodiment illustrated in FIG. 10 is suitable foroperation by a gynecologist in conjunction with colposcopy. In thissetting, the device is well adapted for use as an assisting device fordetermining which areas of the cervix may require biopsies.

[0095] In one embodiment, the systems and methods of the inventionprovide a hand held device adapted for illuminating a target tissue withwhite light and further adapted for detecting reflections orbackscattered responses at three specific wavelengths. FIG. 11 shows anillustrative embodiment suitable for screening applications. Thisembodiment provides features of a visualization colposcope and featuresof a screening device according to the present invention. In thisembodiment, a superimposition of pathological findings on a cervicalimage may be produced.

[0096]FIG. 11 shows a colposcreener 1330 consisting of two orthogonaloptical paths 1331 and 1332. The first optical path 1331 includes aplurality of lenses (for example lenses 1333, 1334 and 1335) to imagethe tissue 1336 so that it can be viewed by an observer 1303. The secondoptical path 1332 includes a distal portion 1331 a of the first opticalpath 1331 (for example lenses 1334 and 1335), a beam splitter, 1338 andadditional lenses (for example, lenses 1337 and 1339). The beam splitter1338 couples the two optical paths 1331 and 1332 to the distal portion1331 a of the first optical path 1331, directing half of the lightreflected back from the tissue 1336 to be viewed by the observer 1303through the ocular 1333 and directing half to a sensor 1340. The sensor1340 is coupled to the optics via a mirror 1341, as shown in FIG. 11, orthe sensor 1340 is positioned in the image plane of the second opticalpath 1332. In some embodiments, the sensor 1340 comprises a plurality ofsensors corresponding in number to the number of preselectedwavelengths, so that one sensor is dedicated to each wavelength. Thesensor 1340 can be, for example an ICCD, a standard CCD, or any otherdetector system known in the art or envisioned by those of ordinaryskill in these arts. A filter wheel 1342 is placed in the optical pathof the detected beam 1332, to allow at any given time only onewavelength to reach the detector 1340. The filter wheel 1342 is mountedon an appropriate driving mechanism, for instance, a stepper motor 1343,which sequentially indexes the wheel to the appropriate filter.

[0097] Arrangements of filter wheels are shown in more detail in FIGS.12A, 12B and 12C. In one embodiment, as shown in FIG. 12A, the filterwheel 1442 has three filters, 1444, 1445 and 1446, each capable ofblocking most of the spectrum of the reflected beam except around thethree selected wavelengths, 380 nm, 480 nm and 650 nm respectively (forthe filters 1444, 1445 and 1446). It will be understood by those ofordinary skill in the art that a number of duplications of these filterscan be employed for drive simplicity, in particular when the crosssection of the reflected beam is narrowed (at the common focal point ofthe lens on both sides of the filter wheel 1442), so as to allow morethan three wavelengths to be determined per rotation of the filter wheel1442. In such an arrangement in the illustrated embodiment, the numberof filter slots would be a multiple of three. In another embodiment, asshown in FIG. 12B, a different filter wheel, 1447, is used in place ofthe previously illustrated filter wheel 1442. The filter wheel 1447 hasfour positions (or multiples of four). The first three, positions 1448,1449 and 1450, are filters transmitting at 380 nm, 480 nm and 650 nmrespectively, as cited above, and a fourth slot, 1451, being spectrallyneutral, namely it is either a simple open slot in the filter wheel1447, or a neutral filter that reduces the transmission of allwavelengths by a constant factor. The latter case simplifies the task ofmaintaining the signals received by the sensor 1440, (for instance aCCD) under a given threshold and thus preventing sensor's saturation.

[0098] In one embodiment, the shape of the colposcreener 1330 is similarto the device depicted in FIG. 11. FIG. 11 shows a colposcreener 1330shaped like a gun, with a trigger 1352 used to initiate the processes ofobtaining optical reflection data and viewing the tissue 1336. Inoperation of this embodiment, pressing the trigger 1352 switches on alight source in the control console 1353. This light source isconcentrated into an optical fiber bundle (not shown) included in thecontrol cable 1354 which connects the control console 1353 and thecolposcreener 1330. The optical fiber bundle 1354 delivers light to thedistal end 1355 of the colposcreener 1330. In one embodiment, a cone ofwhite light 1357 illuminates the target tissue 1336 homogeneously. It isunderstood that other shapes and configurations of the colposcreener1330 may be envisioned by those of ordinary skill in these arts withoutdeparting from the scope of the systems and methods disclosed herein.Furthermore, while the colposcreener 1330 is adapted for examination ofthe cervix, other shapes and embodiments consistent with these systemsand methods may be devised that are structurally adapted for otheranatomic areas.

[0099]FIG. 11 further shows that light reflected from the tissue issplit by the beam splitter 1338 into a viewing beam carried along theoptical path 1331 and a detection beam carried along the optical path1332. In that manner, the tissue screened is viewed directly through theocular 1333 while the detection beam is being sequentially scanned forthe three wave length discussed above. The tissue is imaged onto thesensor 1340. In one embodiment the sensor 1340 comprises a CCD array,whereby the light intensity reflected for each point in the tissue ismeasured. The data from the sensor 1340 is transmitted through a datacable 1356 to a data processing unit 1358 for further analysis.Synchronization signals generated in the control console 1353 providecorrect indexing of the streams of data for each one of the threefilters in the filter wheel 1342. This may be achieved by using thesignal sent to the stepper motor 1343 to coordinate with the data streamfrom the sensor 1340.

[0100] In one illustrative embodiment, the synchronization task issimplified by using the geometry of the filters in the filter wheel1342. In this embodiment, the motor 1343 is used in a continuous ratherthen a stepping manner, thus the filter wheel 1342 rotates continuously.An embodiment using a filter wheel in this way is shown in FIG. 12C,where the filter wheel 1459 is depicted as having three unequal filters1460, 1461 and 1462, separated by unequal spaces 1463, 1464 and 1465. Inthis embodiment, a CCD or a CCD array is advantageously employed as thesensor 1340, as previously described. Since the CCD has its highestsensitivity in the red part of the spectrum, and the light source istypically richer in the red part of the spectrum as well, the 650 nm redfilter 1460 in FIG. 12C is much shorter, with shorter collection time.The short collection time is used to indicate to the data analysis unit1358 that the red filter 1460 (transmitting selectively at 650 nm) isbeing used. To better equilibrate the intensities received, the greenfilter 1461 (transmitting selectively at 480 nm) is larger than the redfilter 1460. The blue filter 1462 (transmitting selectively at 380 nmwhere the CCD is least sensitive) occupies the longest segment of thecircumference. The signals received at various wavelengths are morehomogeneous and easier to analyze. Integration times can be adjustedaccordingly. The adjustment of the integration time can be keyed on the“No signal” periods between the filters, represented by the unequalspacings 1463, 1464 and 1465 between the filters.

[0101] In this illustrative approach, the actual normalized intensities,R₃₈₀, R₄₈₀ and R₆₅₀ as discussed above are modified to account for thetime variability of data acquisition between the three differentfilters. Since these factors depend on the specific integration timeselected, the normalized reflections R_(λ) provided above are used,understanding that algorithms based on these findings are devised once acalibration for a specific design is available.

[0102] The data received for each one of the three filters is analyzedfor each pixel and is displayed on the display monitor 1322 in a dualfashion. The first display generates a Red/Green/Blue image of thetissue by taking the raw data (normalized for spectral differences inthe CCD sensitivity as well as variations of integration times whenusing the filter wheel 1559 shown in FIG. 12C) from each CCD's pixel andpresenting it as a normal full color picture. This is achieved with wellknown “frame grabbing” electronics readily available commercially.

[0103] Each pixel, P_(ij), has associated with it three values (residingin the grabbed frame), I_(ij,380), I_(ij,480) and I_(ij,650), from whichare derived normalized intensities R_(ij,380) and R_(ij,650). A stronglydiscriminating algorithm selects all pixels P_(ij) for which both of theconditions R_(ij,650)>3.3 and R_(ij,380)<0.9, namely those pixels forwhich a pathology is identified. These pixels form a group Qij ofpathological tissue. A “weaker” discrimination defines as “pathological”only those P_(ij) for which R_(ij,650)>3.3 and the so defined Q_(ij) arethen painted on the total image as a pathology.

[0104] The display superimposes an image of all the pixels Q_(ij) havinga “pathological” signature on the natural picture of the tissue. This isachieved by selecting a color uncommon to the tissue (such as green, orblue) and painting said all Q_(ij) (pathological) pixels all in the samecolor, thus obtaining an artificial-looking image of the extent of thepathology in the tissue. The filter depicted in FIG. 12B wherein one ofthe positions is a neutral filter uses the image generated by theneutral filter (which will appear on the display having shades of gray)as the background image of the tissue on which the pathology issuperimposed in any desired color. To maintain the system of FIG. 11 incalibration, a standard white reflector is used, as described above forFIG. 10.

[0105]FIG. 13 depicts an embodiment in which the apparatus is configuredas a screening device 1570 without providing for direct visualization ofthe tissue being screened by an observer. In the illustrativeembodiment, the screening optical head 1571 contains an optical train1572, an illuminator 1573, a CCD array 1574 and a filter wheel 1575. Thefilter wheel 1575 is rotated as previously described, eithercontinuously or in a stepping fashion with a stepper motor, 1576. Thelight source is within the data processing/control console 1577, and thedata is displayed on a display 1578. Light from the light source iscollected into a bundle of optical fibers 1573 which is an integral partof the cable 1579, between the console 1577 and the screening device1571. While FIG. 13 shows the optical fibers 1573 as nested within thescreening device 1571 at one location, it is understood that the fiberswithin the bundle can be arranged circumferentially or in any othergeometric arrangement in order to provide homogeneous illumination ofthe target tissue 1580.

[0106]FIG. 14 shows a filter wheel 1600 suitable for use with the devicedepicted in FIG. 13. The illustrated filter wheel 1600 is configured toselect light at wavelengths of 380 nm, 480 nm and 650 nm, with an openarea to facilitate synchronization. It should also be evident topractitioners of ordinary skill that any arrangement of filter wheelsunderstood in the art, including those illustrated in FIGS. 12A-12Cabove, could be used in the depicted system, with the operation of theapparatus being adjusted accordingly.

[0107] In operation the screening device 1571 may be pointed to thetarget tissue 1580. The tissue may be illuminated through the opticalfiber bundle 1573 and reflections from the tissue may be recorded by theCCD array 1574 at about 380 nm, about 480 nm and about 650 nm. The dataprocessing unit 1577 analyzes the recorded data using any one of thealgorithms described above. Tissues with color enhanced pathologies arerepresented on the display 1578. In one embodiment of the invention,visual display is not provided and only a reading or printout of thestatus of the subject (having or nor having a pathology in the targettissue) is presented. In this embodiment, the instrument advantageouslyuses the above-mentioned tissue integral algorithm. To use thisalgorithm, the data processing unit 1577, after obtaining the valuesR_(ij,650) and R_(ij,380) for each pixel P_(ij), determines the maximumand minimum values obtained for R₆₅₀ and R₃₈₀. If the conditionsR₆₅₀(max)<1.2R₆₅₀(min) and R₃₈₀(max)<1.2₃₈₀(min) are met, the subject isclassified free of pathologies. Otherwise, the subject is referred foradditional diagnostic evaluation to determine the nature and the extentof the suspected pathology.

[0108] In another illustrative embodiment, depicted in FIG. 15, thefilter wheel is eliminated. Further, in lieu of a standard CCD array acolor CCD array may be used. In the illustrated embodiment, a screeningdevice 1700 includes three modules 1701, 1704 and 1703. The firstmodule, the screening probe 1701, is operably connected to the secondmodule 1704 through a cable 1703. The first module 1701 contains acircumferentially arranged optical fiber bundle 1706 for transmittinglight to a target 1710, and an optical path 1702 comprising opticalelements for receiving light emitted from the target 1710. The secondmodule 1704 contains a light source coupled to an optical fiber bundle1706. The fibers in the optical fiber bundle 1706 are distributedcircumferentially at the distal end of the probe. Furthermore, thesecond module 1704 contains a data processing unit, including anelectronic frame grabbing submodule to process data received from thecolor CCD array 1900 in the probe module. Results are displayed on thedisplay module 1705, which is connected to the second module 1704 by wayof cable 1707.

[0109] The color CCD array 1900, as used in the illustrated embodiment,may be typically divided into pixels each having four elements. FIG. 16shows a segment of the surface of such an array 1800. For illustrativepurposes, an array of 10×10 elements organized as an array of 5×5 pixelsis shown. However, it is understood that such an array can comprise inexcess of 500×500 elements and thus more that 250×250 pixels. Each oneof the pixels 1801 has two green filters 1802 and 1803, overlaying twoof the elements of the four elements in pixel 1801. The other twoelements, 1804 and 1805, have respectively a red and a blue filteroverlaid thereupon. While the specific filters employed in standardcolor CCD devices can vary from the three wavelengths selected above,and can vary from manufacturer to manufacturer, standard color CCDs canbe used in the invention.

[0110] The operation of the device 1700 depicted in FIG. 15 is similarto the operation of the system depicted in FIG. 13, except that nofilter wheel is employed. In contrast to the embodiment depicted in FIG.13, in the embodiment of FIG. 16 the whole image in three chroma istaken at once, and the frame grabbing module transfers the intensitiesreceived for each one of the three colors to a data processing devicewhich undertakes the normalization of the long and short wavelengthreflection with the middle of the spectrum responses and proceeds toapply to the two artificial intensities so derived any of the previouslydescribed algorithms.

[0111] In the illustrative embodiment, the system optics 1702 images thetarget tissue 1710 onto the color CCD 1800, and the signal from eachpixel is captured in a frame grabbing device in module 1704. Theintensities registered for the two green filtered elements are averagedand used as the normalization value for the intensities registered forthe red filtered element and for the blue filtered element. In thisfashion, normalized values R_(ij)(B) and R_(ij)(R) are obtained for eachpixel having a row i and a column j. These normalized valuesrespectively represent the normalized reflected intensities in the blueand red part of the spectrum.

[0112] While the filters used in commercial color CCD do not correspondexactly to the wavelength 380 nm and 650 nm mentioned above, andfurthermore the bandwidth of those filters are relatively wide,satisfactory calibration and discrimination between pathological andhealthy tissue can be achieved. The threshold values can be changed forR(B) and R(R) relative to those shown above for R₃₈₀ and R₆₅₀. Thesevalues vary somewhat depending on the source of the color CCD. Toalleviate the problem of variability, an array of filters with theappropriate fixed wavelengths of about 380 nm, about 480 nm and about650 nm can be overlaid over a standard CCD array to obtain a screeningdevice that has no moving parts (such as the filter wheel) in some ofthe embodiments mentioned above. The general algorithmR_(λ)(Max)<αR_(λ)(min) where α>1 and is a function of the specific λselected is advantageously employed without undue experimentation byordinary skilled practitioners in these arts to discriminate betweenhealthy and pathological tissue.

[0113] In another illustrative embodiment, these systems and methods areused in conjunction with an acetic acid delivery system, as shown inFIG. 17. FIG. 17 shows a graph 1900 of measured reflectance I, asnormalized by the initial reflectance immediately after the applicationof the acetic acid, as function of time, beginning with the applicationof an acetic solution to the cervix, at a wavelength of about 600 nm.The graph 1900 has normalized intensity plotted along the vertical axis1902 and time expressed in seconds plotted along the horizontal axis1904. The data collection is achieved by using a single narrow bandpassfilter, transmitting around 600 nm, overlaying a CCD. FIG. 17 representsmeasurements from a number of tissue samples (in vivo, followed bydetermination of the pathology from biopsies). The time dependence ofthe reflected responses from tissues fall into three well differentiatedzones. Healthy (NED) tissues have a response 1910 which is independentof time. CIN II tissue shows an increasing response 1920 for about 30 to60 seconds, and then the reflectance slowly fades out and returns tonormal within about three minutes or a little longer. CIN III tissueshows a response 1930 that includes a strong change with time for thefirst 20 to 30 seconds, and then, the response 1930 stays strong forlonger than about three minutes. FIG. 17 shows that the optical behaviorof the various tissue classes following the application of theamplifying agent differentiates between healthy and pathologic tissuefrom measurements taken during the first 10 to 20 seconds after theapplication of the amplifying agent (or chemical agent), in this caseacetic acid.

[0114] A useful algorithm employs the rate of change of the normalizedintensity I with time, dI/dt at between 10 to 20 seconds after theapplication of the amplifying agent. According to this algorithm, ifdI/dt<0.055 sec⁻¹, the tissue is classified as healthy (NED). If 0.075sec⁻¹<dI/dt<0.11 sec⁻¹, the tissue is classified as CIN II. Ifdi/dt>0.11 sec⁻¹, the tissue is classified as CIN III. In oneembodiment, the higher dI/dt during the first 10 to 20 seconds after theapplication of the acetic acid solution, the more severe the pathologyis.

[0115] In another embodiment, an algorithm involves the measurement ofthe normalized reflectance after either 10 or 20 seconds from theapplication of the acetic acid solution. If I is greater than 1.25 after10 seconds (or about 1.5 after 20 seconds), the tissue is classified aspathologic, and the patient is directed to have a more detailed analysisof the condition, sometimes, including a biopsy. This embodiment isapplied, as an example, when the probe is used in true screeningsituations rather than in more traditional colposcopic examinations.

[0116] In another embodiment, an algorithm is based on the time requiredto reach a maximum in the back reflected response of the tissue.According to this embodiment, the longer it takes to reach this maximumthe more severe the condition, providing, however, that the maximum ismore than about 3.0 times the minimum back scattered response for thesame tissue. The disadvantage of this approach is that longer exposuremay be required, particularly in the case of CIN III, where backscattered responses continue to increase even after more than 200seconds.

[0117] To shorten that time interval, another algorithm compares themaximum normalized response at 600 nm during any interval of timegreater than 10 seconds from the application of the acetic acidsolution, to the initial response, and if that response is more than 30%larger than the initial response, the tissue is classified as CIN ingeneral. This algorithm is used when fast classification of cervixes ina screening environment is desired.

[0118] In yet another embodiment of the invention, a screening algorithmtakes an ititial reading of responses for each point probed prior to theapplication of the acetic acid, stores the values as a standard set, andthen takes a number of images sequentially. The screening algorithmperforms a point by point subtraction of the value of the stored initialresponses from the responses obtained after the application of theacetic acid. The time dependence for various classes of tissue resultsin distributions similar to those shown in FIG. 17, except that thescale of the back scattered intensities is now changed. The algorithmutilizes the differential responses of various classes in a manner asdescribed above.

[0119] Apparatus and methods for controlled delivery amount and deliverypattern of a chemical agent are disclosed below. Apparatus and methodfor accurately and synchronously triggering the optical measurementswith regard to the time of delivery of the chemical agent are disclosedbelow. Image capture software to record time-stamped images anduser-defined regions of interest to be defined on a master image isdisclosed below. This analysis software automates the calculation anddisplay of acetowhitening characteristics from a motion correctedtime-sequence of patient images. This improves the ability to correlateinstrument measurements to the pathological evaluation of biopsiedtissue.

[0120] In another embodiment of the invention, when using an amplifyingagent such as acetic acid, an automated system delivers the amplifyingagent to the target tissue. A triggering mechanism applies the chemicalreproducibly and eliminates variability of time delays between theapplication and the start of obtaining optical responses from the targettissue.

[0121]FIG. 18A shows an illustrative embodiment of a system with ascreening probe 2000 similar in construction to the probe shown in FIG.11. This apparatus and the associated techniques are used to improve thedemarcation of a start time and to guarantee the application of aconstant volume of acetic acid. Another embodiment is a mucosal atomizerdevice comprising a 3 cc syringe and 6″ stylet tubing extension nozzleis used to spray 2 cc of 5% acetic acid uniformly onto the surface ofthe cervix. The distal part 2013 of the probe 2000 is covered with acomposite disposable sheath 2011 having attached at its own distalperiphery a hollow toroidal structure 2014. The hollow toroidalstructure 2014 contains the chemical agent or amplifying agent, forexample, acetic acid at a 3% to 5% concentration. A retractor 2012compresses the toroidal structure 2014 when the operator is ready toapply the amplifying agent to the target tissue. When the retractor 2012is retracted using a trigger like mechanism 2015 the toroidal structure2014 is compressed and its content is sprayed onto the tissue.Simultaneously with the retraction action, the probe is signaled tostart taking measurements, by the actuation of a switch 2016 in thehandle of the probe. The chemical or amplifying agent is sprayed througha plurality of perforations 2017 as shown in FIG. 18B, a top view of thedistal end of the assembled sheath/retractor assembly. To preventaccidental expulsion of the solution, a covering such as an adhesivetape may be attached to the distal end 2013, covering the toroidalstructure 2014. In one embodiment, the covering may be removed aftermounting the sheath on the probe and just prior to the insertion of theprobe into a target area such as the vagina. While FIG. 18A depicts acylindrical structure, it should be apparent to those of ordinary skillin the art that a conical structure can be utilized to improve packagingand nesting of multiple disposable sheaths, and furthermore, that anyother structure may be constructed which is adapted for the functionsdepicted in FIG. 18A and which is further adapted for the anatomicregion in which it will be used.

[0122] In the embodiment shown in FIGS. 18B and 18C, the retractor 2012is designed to leave an optical window 2018 for the reception ofresponses from the illuminated tissue. Illumination is achieved throughcircumferentially distributed optical fibers, as previously described inFIG. 11, and the light is transmitted through a transparent part of theperipheral distal end of the retractor 2012. The toroidal container 2014for the amplifying agent is affixed to the sheath 2011, as shown in FIG.18C, which shows a cross sectional view of the distal end of thesheath/retractor assembly. The toroidal container 2014 is directlyattached to the sheath (as shown at 2019) or it is affixed in any othersuitable way.

[0123] While in FIG. 18A we show a toroidal container 2014 whichdischarges its content upon compression, it should be clear that othershapes may be useful in the practice of the invention. For instance, thecross section of the toroidal container 2014 can be oval or rectangular.In certain embodiments, the amplifying agent container is constructed asa side mounted syringe having a plunger that causes discharge of theamplifying agent in a spray form, while providing simultaneously asignal to the probe that amplifying agent is applied, and thus providinga starting point for the temporal measurements of reflections from thetarget tissue. While this figure shows one embodiment of a system forautomating the application of the amplifying agent and for standardizingthe time lapse between the application of the amplifying agent and themeasurement of back scattered responses from the target tissue, itshould be evident to a person trained in the art that other mechanismsachieving the same goal can be devised without deviating from the spiritof the invention. Such other applicators could include, but are notlimited to, surgical applicators like sponges, cloths or swabs that aremade to be retracted after the application of the amplifying solution soleave open the optical path between the tissue and the probe's distalend 2013.

[0124] When the algorithms use normalized responses, as normalizedagainst time zero, the trigger actuates a timer within the probecontroller that sets up a predetermined time interval for the firstmeasurement (typically within 1 second of amplifying agent application).When the algorithm used normalizes responses relative to the responseobtained prior to the application of the amplifying agent, an image ofthe cervix is taken prior to the application and recorded with the framegrabber in the data processing unit 1704. After the trigger 2016 signalsthe probe to start taking responses, the responses are taken andnormalized (pixel by pixel) and one of the algorithms described aboveanalyzes the data. The data are presented as either a “positive” or“negative” finding for the whole cervix, or alternatively, an artificialimage of the pathology is presented for those pixels where thealgorithms returned positive findings. This image is superimposed on avisual image of the cervix and recorded to allow post screening accuratelocation of tissue requiring subsequent biopsy.

[0125] In some embodiments of the invention, spatial data are averagedover groups of neighboring pixels (between 2×2 to 6×6), and theseaverages (both for the standardizing measurement, or normalizingmeasurement) are used as normalized intensities. Other methods foraveraging or normalizing spatial data can be used. Different methods ofnormalizing can be related to the resolution of the CCD used in thatspecific interest.

[0126] In another embodiment, a plurality of chemical agents are appliedto a specimen, either simultaneously or sequentially. The use ofmultiple chemical agents causes any of a number of different effects.One chemical agent is used to control or change pH (e.g., hydrogen ionconcentration), change the concentration of one or more other ionicspecies, or change an osmotic pressure, while another chemical agent isused to induce another sort of change, for example, staining a material,activating or passivating a material, or otherwise changing a physicalproperty of a material.

[0127] Application of an exogenous contrast agent when combined with theactivation of an endogenous contrast agent gives rise to a combinedcontrast that provides more valuable information than either agentalone. For example, application of acetic acid to epithelial tissueresults in time-dependent effects in the fluorescence emission spectrumresulting from activation of endogenous native fluorophores in tissue,such as NADH, collagen, elastin, favins (e.g., FAD) or porphyrins.

[0128] This effect arises from at least two different sources. Onesource is the penetration of the acetic acid into the tissue followed bythe resulting pH change on the spectral properties of the endogenousfluorophore. The effect of pH is shown for NADH in FIG. 19. FIG. 19shows four absorption spectra recorded over the wavelength range ofabout 320 nm to aboout 600 nm. The absorbance is plotted along thevertical axis 2102 and the wavelength in nm is plotted along thehorizontal axis 2104. A baseline spectrum 2110 is taken for a 5% aceticacid solution containing no NADH, and shows little absorption. Thespectrum 2120 is taken at pH 4.0 and shows two strong absorption peaksat approximately 350 nm and at approximately 430 nm. The spectrum 2130is taken at pH 5.0 and shows a strong absorption peak at approximately350 nm and a much weaker absorption peak at approximately 430 nm ascompared to the pH 4.0 spectrum. The spectrum 2140 is taken at pH 7.0and shows a strong absorption peak at approximately 350 nm and virtuallyno absorption peak at approximately 430 nm as compared to the pH 4.0spectrum. The spectral properties of NADH absorption are significantlyaffected by pH. Since absorption is the first step in fluorescence, itis reasonable to expect that pH will affect the emitted fluorescence aswell.

[0129] Acetic acid penetrates into different types of tissues and cellsat different rates depending on the type of tissue present. In addition,the amount of NADH in cells typically differs according to the type ofcell and its metabolic state. Consequently the kinetics of this pHresponse can be indicative of the tissue or cell type and its metaboliccondition.

[0130] Acetic acid causes acetowhitening when applied to certaintissues, such as epithelial surfaces. The acetowhitening effect isproduced by light scattering changes. These changes have furthersecondary effects on spectral measurements, such as inducedfluorescence. Changes in the induced fluorescence result from either oftwo sources. One source is the direct effect of acetowhitening on thepenetration of the UV excitation light. A second effect results from thelight scattering on the observed spectral shape of the emittedfluorescence. Since the acetowhitening is time dependent, thesesecondary effects are time dependent as well.

[0131] Temporal changes observed in fluorescence emission following theapplication of acetic acid to a cell suspension is shown in FIG. 20.FIG. 20 shows a graph 2200 of spectra measured as a function of timeafter application of acetic acid. The spectral intensity is plottedalong the vertical axis 2202 and the wavelength in nm is plotted alongthe horizontal axis 2204. Curve 2210 is recorded at the time ofapplication of the acetic acid solution. Curve 2220 is measured 0.5minutes after acetic acid application. Curves 2230, 2240, 2250 and 2260are recorded 1, 2, 3, and 7 minutes after acetic acid application,respectively. The fluorescence spectrum originally observed at the timeof acetic acid application quickly decreases in intensity, and recoversslowly thereafter.

[0132] Fluorescence spectra in cervical tissue have similar changes overtime following application of acetic acid. FIG. 21 is a graph 2300 thatshows the changes in fluorescence spectra before (curve 2310) and after(curve 2320) application of acetic acid to cervical tissue. The spectralintensity is plotted along the vertical axis 2302 and the wavelength innm is plotted along the horizontal axis 2304. Note that the fluorescentintensity at some wavelengths below about 450 nm changes moresubstantially than the intensity at wavelengths above about 450 nm.Since the time-course of those changes is related to the type of tissuebeing probed (as described above) these spectral differences candifferentiate the type of tissue under study.

[0133] Relative motion between the patient and the colposcope can causeproblems with registration of the different images for that patientduring analysis. A robust motion detection and correction technique isdisclosed below. This technique uses the cross-correlation of twosuccessive images to determine global motion. The cross-correlation iscomputed in the Fourier domain using a fast Fourier transform. In oneembodiment, the image registration technique is used after the specimendata is collected. In an alternative embodiment, systems according tothe invention incorporate the technique on-line, as it does not requireexcessive processing overhead.

[0134] Image processing is used to extract relevant features from thedata observed and recorded using the systems and methods of theinvention. Image processing techniques that can be applied include, butare not limited to, color space transformations, filtering, artifactdetection and removal, image enhancement, extraction ofthree-dimensional shape information, manipulations using mathematicalmorphology, and segmentation.

[0135] A color space transfromation is intended to transform the threeprimary colors, red (R), blue (B), and green (G), into a new set ofcolors or values using a kinear or non-linear transformation. A numberof well-known transformations interconvert R,G,B andluminance/chrominance components, for example, as used in convertinglight recorded in a camera into broadcast signals, and renderingbroadcast signals on a television display.

[0136] Filtering is useful in image processing, and is used to suppressnoise and unwanted interfereing signals. Many filters and filteringprocesses are known. Filters can include both hardware filters such asoptical filters and electronic filters, as well as filters applied insoftware, such as digital filters. For example, the median filterreplaces every pixel of an image with the median value computed in agiven neighborhood of the pixel.

[0137] Artifact detection and removal is used to eliminate spuriousinformation from a set of data to be analyzed. Some artifacts, such asportions of an optical field of view that are extraneous, may beeliminated by changing the height and or width of the field of view, orby masking portions of the field of view, for example when a physicianobserves that the field of view includes material that is not ofinterest.

[0138] Image enhancement can include processing to improve the visualcontrast between adjacent portions of an image. A number of knowntechniques are available, including applying a weighting function to arange of intensities or gray scale values.

[0139] Extraction of three-dimensional shape information is useful inrepresenting a surface that is non-planar in two-dimensions. An exampleis computing the three-dimensional features of the cervix to account forthe nonuniformities of illuminating a three-dimensional surface.

[0140] Manipulations using mathematical morphology are well-known. Imageprocessing using the principles of mathematical morphology provides arepresentation of an image in a form that simplifies the computationalburden in image processing.

[0141] Morphological operators are based on the mathematics of settheory. A set in mathematical morphology represents the shape of anobject in an image. In the case of two- dimensional (binary) images, thesets are members of Z² and each element represents the (x,y) coordinatesof a black (or white, depending on the convention) pixel in the image.Gray-scale, color, time-varying components, or any vector-valuedinformation can be included by extending the Euclidean space size.

[0142] The basic morphological operators are described in terms ofgray-scale images below. Let the input image be described by a functionf: Z²→R. Gray-scale dilation is defined as:

(f⊕b)(v,w)=max{f(v−x,w−y)+b(x,y)|(v−x,w−y)εD _(f);(x,y)εD _(b)}

[0143] where b: Z²→R is a function called a structuring element, D_(f)is the domain of f and D₆ is the domain of b. The structuring elementhas a key role in this operator: it is added morphologically to theimage at each pixel location.

[0144] The opposite of dilation is erosion. The erosion operator isdefined as:

(f⊕b)(v,w)=min{f(v+x,w+y)−b(x,y)|(v+x,w+y)εD _(f);(x,y)εD _(b)}

[0145] In this case the structuring element is subtractedmorphologically from the image at each pixel location.

[0146] Two important morphological operators are defined using erosionand dilation: opening and closing. They are respectively defined as:

f∘b=(fΘb)⊕b

f•b=(f⊕b)Θb.

[0147] The effect of opening is to preserve holes and remove peaks,while closing preserves peaks and closes holes according to thestructuring element's shape. The structuring element b is fitted frominside (below an image) in the opening case and fitted from outside(above an image) in the closing case.

[0148] A morphological filter can be defined as any combination ofmorphological operators. For example (f∘b)•b, opening followed byclosing, or (f•b)∘b, closing followed by opening. These operators areneither commutative, nor associative or distributive and the filteringoperators cited above are not equal. One of the following two filters isused:${{f\_ b} = {\frac{1}{2}\left\lbrack {\left( {f \cdot b} \right) + \left( {f \circ b} \right)} \right\rbrack}},{and}$${f\_ b} = {\frac{1}{2}\left\lbrack {{\left( {f \circ b} \right) \cdot b} + {\left( {f \cdot b} \right) \circ b}} \right\rbrack}$

[0149] where the _ symbol means f filtered by b.

[0150] A more elegant way to achieve a morphological filtering withbetter geometrical characteristics is to use geodesic reconstructionafter a morphological opening. The reconstruction process uses geodesicdilation which for gray-scale images is defined by:

(f⊕b)⁽¹⁾(v,w)=min{(f⊕b)(v,w),f ₀(v,w)},(v,w)εD _(f),

[0151] where f₀ is the reference image, usually the original image, andg is a small structuring element, usually a four pixel (cross) or eightpixel (square) connected element. Geodesic reconstruction is obtained byrepeating the geodesic dilation n times ((f⊕b)^((n))) until idempotencyis reached. The geodesic reconstruction is then written:

Rg=(f⊕b)^((i)), with (f⊕b)^((i+1))=(f⊕b)^((i))

[0152] An equivalent operator can be defined for reconstruction aftermorphological closing which uses geodesic erosion. For gray-scale imagesit is defined as:

(f _(—) b)⁽¹⁾(v,w)=max{(f _(—) b)(v,w),f ₀(v,w)},(v,w)εD _(f)

[0153] An example of geodesic reconstruction after morphological openingsuppresses the square shape deformation introduced by the openingprocess. These geodesic reconstruction operators significantly improveany filtering process for a modest additional computation time.

[0154] The most natural example of diffusion process is heat transferinside matter. This physical phenomenon is mathematically expressed bythe following partial differential equations:

q=−k∇T,${{c\quad p\frac{\partial T}{\partial t}} = {{{- \nabla} \cdot q} + f}},$

[0155] leading to the following second order elliptic equation:${{c\quad p\frac{\partial T}{\partial t}} = {{{- \nabla} \cdot \left( {k{\nabla T}} \right)} + f}},$

[0156] Heat transfer involves a thermal flux q. The whole system mustobey the law of energy conservation. The symbol ∇ is the differentialoperator, which is defined as ∇=(∂/∂x₁, . . . , ∂/∂x_(d)). The parameterp is the density of the medium, k is the thermal conductivity, c is thespecific heat capacity, and f the capacity of internal heat sources. Ananalogy exists between temperature variation and value variation inimages. The basic formulation is obtained when the medium is assumed tobe homogeneous, without sources and with constant conductivity.

[0157] In image processing applications the ideal objective is to obtainan image where only strong edges are preserved while noise and smallstructures are smoothed out. Diffusion is used as an edge preservingfiltering method. The thermal conductivity is replaced by a conductivityfunction which adapts the diffusion to the local gradient: decreasingdiffusion for increasing gradient. The above diffusion equation becomes:${\frac{\partial v}{\partial t} = {\nabla{\cdot \left( {D{\nabla u}} \right)}}},$

[0158] where v(x, t) is the signal value at time t and position x, and Dis a conductivity matrix. The latter defines the type of diffusion:

[0159] if D reduces to a constant value k then the diffusion isisotropic,

[0160] if D reduces to a nonlinear function g(·) then the diffusion isnonlinear isotropic,

[0161] if D is a tensor whose elements are functions g_(ij)(·) then thediffusion is anisotropic.

[0162] The analysis uses the case where D=g(·), and the followingconductivity function:${g\left( \left| {\nabla\upsilon} \right| \right)} = \left\{ {\begin{matrix}{\quad 1} & {\left. {if}\quad \middle| {\nabla\upsilon} \middle| {\leq k_{o}} \right.,} \\{\quad \left. {k_{o}/} \middle| {\nabla\upsilon} \right|} & \left. {if}\quad \middle| {\nabla\upsilon} \middle| {> {k_{o}.}} \right.\end{matrix},{{\Phi \quad {f\left( \left| {\nabla\upsilon} \right| \right)}} = \left\{ {\begin{matrix}1 & {\left. {if}\quad \middle| {\nabla\upsilon} \middle| {\leq k_{o}} \right.,} \\0 & \left. {if}\quad \middle| {\nabla\upsilon} \middle| {> {k_{o}.}} \right.\end{matrix},} \right.}} \right.$

[0163] Histogram equalization re-assigns pixel values in order to obtaina uniform distribution. Let v(x) be the pixel value at location x andP(v) be the probability density function associated to v. The followingtransformation is used:

ν_(eq)(ν)=∫₀ ^(ν) P(s)ds

[0164] where 0≦v≦1. In the discrete case, the uniform distribution isonly approximated and the following equation is used:${u_{k,{e\quad q}} = {\sum\limits_{j = 0}^{k}\frac{n_{j}}{n}}},$

[0165] where n is the total number of pixels and n_(j) the number ofpixels with value equal to j.

[0166] The fitting technique used is called linear least squares. Theidea is to fit a linear combination of arbitrary functions (linear ornonlinear) given by:${{l(x)} = {\sum\limits_{k = 0}^{M - 1}{\alpha_{k}{f_{k}(x)}}}},$

[0167] where x is an N-dimensional coordinate vector (N=2 in the case ofimages), to a set of data l_(i)(x_(i)), with i=0, . . . , n−1. In oneembodiment, the following series of functions are used:

[0168] 1, x, x², x³, . . . ,

[0169] in the 1-D case and:

[0170] 1, x, y, x², xy, y², x³, x²y, xy², y³, . . . ,

[0171] in the 2-D case.

[0172] The fitting criteria is the minimization of the followingleast-square error:$x^{2} = {\sum\limits_{i - 0}^{n - 1}\left\lbrack \frac{l_{i} - {\sum\limits_{K = 0}^{M - 1}{\alpha_{k}{f_{k}\left( x_{i} \right)}}}}{\sigma_{i}} \right\rbrack^{2}}$

[0173] where σ_(i) is the measurement variance at location x_(i). In oneembodiment, set σ_(i)−1, ∀i=1, . . . , n−1.

[0174] By defining the n×M matrix A whose elements A_(ij) are given by:

A _(ij) =f _(j)(x _(i)),

[0175] and the vector b of length n whose elements b_(i) are given byb_(i)=l_(i), then the following system must be solved:

a=(A ^(T) A)⁻¹ A ^(T) ·b,

[0176] where a=[σ₀ . . . σ_(m−1)]. Since the A^(T)A product is positivedefinite, Cholesky decomposition can be used to compute the inverse.

[0177] Segmentation is a morphological technique that splits an imageinto different regions according to a pre-defined criterion. In theanalysis of the state of health of a biological specimen, it ismeaningful to compare the proprties of different areas of the specimen.Segmentation is a method that directly provides information on how manyregions are present in the image of a specimen, and the location of eachregion.

[0178] In one embodiment, colposcopic images are segmented to trackregions in a time series of images. Relevant features are extracted fromthe labeled regions and their evolution is analyzed as a function oftime, to measure and localize acetowhitening effects. Colposcopic imagesare segmented using a watershed based algorithm. An efficientpre-processing scheme is used, as are two region merging techniques. Theuse of markers to track the segmentation in time-series of images isused, and the problem of global motion and local deformations related tothe precise tracking of these markers is discussed.

[0179] A segmentation scheme for colposcopic images separates the imageof the cervix into a number of regions according to an intensitycriterion. Segmentation techniques are well known in the mathematicalmorphology arts. In one embodiment, the object (e.g., the cervix) isknown and multiple regions with different intensity content within thecervix are to be identified.

[0180] A technique based on the structural and spatial informationrather than on the spectral information is suited to analyze colposcopicimages. One approach uses the watershed technique. The watershedtechnique uses structural information. The watershed technique providesa fine to coarse segmentation of an image in combination with regionmerging techniques. The flooding technique views a gray-level image as a3-dimensional surface and progressively floods this surface from below.Each local minimum in the surface is thought of as a hole. A risingwater level floods a region as soon as a hypothetical water levelreaches the associated minimum. FIG. 22 illustrates this concept on a1-D signal. The arrows 2402 a-2402 d show the flooding origins anddirections and the solid lines 2406 a-2406 d are the watersheds. Theflooded minima are called catchment basins and the borders betweenneighboring basins are called watersheds. Only the catchment basins areof interest. They constitute the segmented image. Fast implementationsuse first-in-first-out (FIFO) queues and sorted data.

[0181]FIG. 23 is a graph 2500 of a signal 2510 and its first derivative2520, both plotted with amplitude as a vertical axis 2502 and positionas a horizontal axis 2504. FIG. 23 shows a signal 2510 used to representwatersheds with three distinct regions (hole 2512, plateau 2514, andpeak 2516) and its derivative function 2520, or gradient. Imagesegmentation with the watershed transform is performed on the imagegradient 2520. The signal shows three distinct regions, and the directwatershed transform would produce a one lowest region. The gradient 2520separates the signal into its three regions. An analogous principleholds for two-dimensional signals, i.e. images.

[0182]FIG. 24 shows a graph 2600 of a sigmoidal scaling function 2610used to enhance the contrast between light and dark regions of an image.The sigmoidal scaling function 2610 is plotted as output along avertical axis 2602 as a function of an input that is indiated along ahorizontal axis 2604. The use of the watershed transform often leads toa severe over- segmentation. Pre-processing is performed to reduce thenumber of regions in a segmented image. In one embodiment, apre-processing scheme reduces the number of regions from severalthousand to several hundreds:

[0183] An algorithm that treats images to provide a segmented imageincludes the following steps:

[0184] computing luminance component L*;

[0185] performing 3-D shape compensation;

[0186] performing sigmoidal scaling;

[0187] morphological closing/opening with a cylindrical structuralelement;

[0188] performing geodesic reconstruction;

[0189] computing image gradient;

[0190] computing threshold gradient;

[0191] performing closing; and

[0192] performing geodesic reconstruction of the gradient image.

[0193] The uniform luminance component L* is well adapted to thesegmentation process, and is computed for an image of interest.

[0194] 3-D shape compensation removes an artifact (e.g., a stair-caseeffect) in the segmented image. The illumination is non-uniform whenfalling on a curved surface (e.g., the cervix), which in turn influencesthe gradient values used for the segmentation.

[0195] A sigmoidal scaling function is used to improve the contrastbetween light and dark regions. FIG. 24 is a graph of a scaled sigmoid,used in this study. The sigmoid increases contrast in the medianintensity range by providing a larger number of quantification levels.Applying a sigmoidal scaling function causes dark and very light areasto exhibit diminished contract, while the limit between light and darkregions is enhanced.

[0196] Closing and opening morphological operators, respectively, areused to suppress small regions corresponding to holes or peaks in theimages. The geodesic reconstruction keeps the geometrical aspect of theimage as close as possible to that of the original image. Amorphological closing with geodesic reconstruction is performed on thegradient of the filtered image to remove plateaus, which are visualizedas separate regions in the watershed transform. The diameter of thecylinder used as structuring element defines the minimum size of theregions in the segmented image.

[0197] Finite-element approximations are used to compute derivatives.Alternative approaches involve using a Sobel operator, which is a3×3filter. Another alternative is the use of a local cubic polynomialapproximation, which is a 5×5 filter. Before processing the gradient forthe watershed extraction, application of a threshold removes smallvalues.

[0198] In one embodiment, the gradient is computed using cubicmean-square approximation. In another embodiment, a morphologicalclosing/opening filter with geodesic reconstruction is first applied andthen the gradient is computed. Spurious regions are smoothed out andcontrast enhanced in large regions by both methods.

[0199] In one embodiment, the watershed algorithm is modified asfollows. The data is represented in floating point, and the intervalsteps between successive flooded levels is not uniform. Also, eachwatershed pixel is merged into a neighboring region according to anearest neighbor criterion.

[0200] The region merging step following the watershed transform stepreduces over-segmentation. In one embodiment, neighboring regions aremerged if an intensity change along their border is greater than a giventhreshold. Alternatively, neighboring regions are merged if a differencein mean intensity value is greater than a given threshold.

[0201] In both embodiments, a map of all border pixels is used. For eachsegment, a computer computes the difference between the mean value ofpixels of each of the two regions under consideration. If the absolutevalue is below a given value, the segment is removed from the mask, andthe regions are merged. The final mask is used to map out the gradientimage. The watersheds are recomputed.

[0202] An alternative merging algorithm uses the same routines. Thealternative algorithm uses the mean value image as input in place of theoriginal image. Since all pixels in a region have the same (mean) value,the algorithm works differently, in that border segments are suppressedif the difference in mean value between neighboring regions is smallerthan a given threshold. A morphological distance is an approximation ofthe distance, in pixels, from a pixel to the nearest segment border. Amethod to use markers to track the segmentation in time series of imagesis now presented. The extraction of markers is necessary in order toinitialize the flooding process in the watershed transform computed insuccessive images (e.g., in time-series).

[0203] The approach used comprises the steps of finding pixels havingminimum value for each region, and selecting the minimum with thelargest morphological distance for each region.

[0204] The first step selects the minimum value as an initial marker,since the flooding used in the watersheds start at local minima. Thepixel with minimum value and largest morphological distance is used toavoid a small deformation of a region pushing a marker outside of theregion.

[0205] A homotopy modification of the gradient image obtained with themarkers is used to suppress catchment basins corresponding to minimathat have not been marked, in order to speed up the computation. Thehomotopy modification of ν is the geodesic reconstruction of ν (mask)from {acute over (ν)} (marker).${\overset{\sim}{\upsilon}\left( {\kappa,i} \right)} = \left( \begin{matrix}{\upsilon \left( {\kappa,i} \right)} & {{{if}\quad \left( {\kappa,i} \right)} \in M} \\{\max \quad y\quad {\upsilon \left( {\kappa,i} \right)}} & {o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad {e.}}\end{matrix} \right.$

[0206] In one embodiment, more than one marker per region is considered.Pixels having a morphological distance greater than a given value(typically 2-3) or being at least equal to the largest morphologicaldistance within a given region are considered. These markers are used tozero out gradient values in the following image, in order to reduceinfluence of local maxima on the homotopy modification. It is assumedthat the borders between neighboring regions are located somewherebetween the marked regions.

[0207] Further, the markers are used to initialize the watershedalgorithm with the gradient image of the next image. Tracking schemesare employed to take into account global and local motion.

[0208] One illustrative tracking scheme for the detection of patientmotion during an acquisition cycle uses the cross-correlation of twosub-images of two successive images to determine the global motion. Analternative algorithm is used to track motion for segmentation tasks.

[0209] Typically, images of specimens exhibit large homogeneous regionswhich are difficult to track. Structural information is used to improvemotion tracking. Derivatives are used instead of the original image.Using the gradient improves the system's sensitivity to glare, while theuse of the sum of the gradients in both the x and y directionshighlights low-contrast structures. Using the Laplacian operator (thesum of second derivatives) provides similar results. Applying a low-passfilter before computing the derivatives yields results similar to theLaplacian of Gaussian used for edge detection. The low pass filtersuppresses noise and smoothes out glare.

[0210] The embodiment further comprises three modifications. The truecross-correlation of successive images is computed in a 512×512 pixelswindow. The two windows used for each image are different and are ofsizes 302×302 and 210×210, respectively. A Hamming window is used toextract these two signals. The two windows have different sizes to makesure that the second signal is completely contained in the first one,and the use of a Hamming window avoids small oscillations, especially atthe transition of the selected signals and the zero-padded areas. Theequations used for the motion detection are given below.

[0211] Optical flow algorithms are used to measure local motions. Inorder to save computation time, the optical flow is computed only forthe markers. Optical flow is defined as the distribution of apparentvelocities of movement of brightness patterns in an image, and is usedto reconstruct three-dimensional surfaces in medical imagingapplications.

[0212] Additional embodiments of motion detection algorithms include thefollowing steps: implementation of a local deformation tracking systemfor improving the precision of marker tracking; extraction of featuresignals from the series of segmentation results; analysis of featuresignals and classification into groups of interest; and use of groupinformation to correlate the evolution with the histology.

[0213] For motion detection, only a single frame is used. The three RGBcolor components are transformed into a single intensity component usingthe following relationship:

I=0.299·R+0.587·G+0.114·B

[0214] In order to suppress high-frequencies due to noise and to theinterlaced video signals, we apply a Gaussian low-pass filter to theintensity component:${g\left( \overset{\rightarrow}{x} \right)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp \left( \frac{\left( {\overset{\rightarrow}{x} - \overset{\rightarrow}{\mu}} \right)^{T} \cdot \left( {\overset{\rightarrow}{x} - \overset{\rightarrow}{\mu}} \right)}{2\sigma^{2}} \right)}}$

[0215] where {overscore (μ)} is the center (mean value) of the Gaussianand σ is its standard deviation. Finally, we use only derivativeinformation to compute the translation parameters, either the sum ofderivatives $\frac{\partial}{\partial x} + \frac{\partial}{\partial y}$

[0216] or the Laplacian$\frac{\partial^{2}}{\partial x^{2}} + {\frac{\partial^{2}}{\partial y^{2}}.}$

[0217] Motion can be detected using a cross-correlation operator appliedto two successive images in a sequence.

[0218] The cross-correlation is computed in the Fourier domain using afast Fourier transform. The following relationship is used:

Φ=X·Y*

[0219] where Φ, X, and Y are the Fourier transform of thecross-correlation function, the first, and the second signal,respectively. The * symbol represents the complex conjugate. Note thatthe cross-correlation of two signals of length N₁ and N₂ providesN₁+N₂−1 values and therefore, in order to avoid aliasing problems due tounder-sampling, the two signals must be padded with zeros up to N₁+N₂−1samples.

[0220] For discrete signals (i.e. sampled and quantized signals), thediscrete Fourier transform (DFT) and the inverse discrete Fouriertransform (IDFT) are given respectively by: $\begin{matrix}{{v\left( {k,l} \right)} = {\sum\limits_{m = 0}^{N - 1}\quad {\sum\limits_{n = 0}^{M - 1}\quad {{u\left( {m,n} \right)}{\exp \left( {- \frac{j2\pi mk}{N}} \right)}{\exp \left( {- \frac{j2\pi nl}{M}} \right)}}}}} \\{{u\left( {m,n} \right)} = {\frac{1}{NM}{\sum\limits_{k = 0}^{N - 1}\quad {\sum\limits_{l = 0}^{M - 1}\quad {{v\left( {m,n} \right)}{\exp \left( \frac{j2\pi mk}{N} \right)}{\exp \left( \frac{j2\pi nl}{M} \right)}}}}}}\end{matrix}$

[0221] This transform expands the signal onto an orthonormal basis ofexponential functions. Once the inverse discrete transform is computed,the location of the maximum value corresponds to the translationnecessary to align both images.

[0222] Different types of windows are used for spectral analysis, whenonly part of a signal is analyzed. The goal is to avoid oscillationaround discontinuities (Gibbs phenomenon). A Hamming window can be used,which is given by the following relationship:

ω_(h)(k)=½[1+cos(2πk/N)]

[0223] where N is the number of samples, k is the sample index, and−N/2<=k<=−N/2. In the frequency domain, the Fourier transform of thesignal is convolved with the Fourier transform of the Hamming window.For the two-dimensional case the Hamming window is constructed as aseparable function, i.e. ω_(h)(k,l)=ω_(h)(k)·ω_(h)(k), where (k,l) arethe pixel coordinates.

[0224] As mentioned above, in some embodiments, the cross-correlation ofthe sum-of-derivatives images is the basis of a motion detectionalgorithm.

[0225] The cross-correlation of two images in a sequence providesinformation about the translation necessary to obtain the best match inthe inner-product sense. However, this does not necessarily mean thatthe two images are perfectly aligned. A validation method is necessaryto measure the “quality” of the matching.

[0226] The Sobel operator is given by: ${fs} = {{1/8}\begin{pmatrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{pmatrix}}$

[0227] This filter is obtained by convolving the finite elementapproximation to derivatives with a weight matrix:${fs} = {{1/2}{{\begin{pmatrix}0 & 0 & 0 \\{- 1} & 0 & 1 \\0 & 0 & 0\end{pmatrix}**1}/4}\begin{pmatrix}0 & 1 & 0 \\0 & 2 & 0 \\0 & 1 & 0\end{pmatrix}}$

[0228] where ** is the two-dimensional convolution. The second filter inEquation 2 is a low-pass filter in the direction perpendicular to thederivative operator, which renders the filter less sensitive to noise.The derivative along the y-axis is obtained by using the transposedversion of the Sobel operator.

[0229] Another way to compute derivatives is to use a local polynomialapproximation by minimizing the mean-square error (MSE) with theunderlying image pixels. The approximation is given by:${\upsilon \left( {\kappa,i} \right)} \approx {\sum\limits_{i = 0}^{8}\quad {b_{i}\quad {\varphi_{i}\left( {\kappa,i} \right)}}}$

[0230] where (k,l) are the coordinates in the local 5×5 domain centeredon the current pixel. The b_(i) coefficients are the optimal weights inthe MSE sense and the Φ_(i) are orthogonal polynomials (1, k, l, k²−2/3,l²−2/3, kl, (k²−2/3)l, (l²−2/3)k, (k²−2/3)(l²−2/3)). The minimization ofthe MSE leads to the following filter for the first derivatives:${f\quad \rho} = {{\frac{1}{50}\begin{pmatrix}{- 2} & {- 1} & 0 & 1 & 2 \\{- 2} & {- 1} & 0 & 1 & 2 \\{- 2} & {- 1} & 0 & 1 & 2 \\{- 2} & {- 1} & 0 & 1 & 2 \\{- 2} & {- 1} & 0 & 1 & 2\end{pmatrix}} - {\frac{17}{300}\begin{pmatrix}{- 1} & 2 & 0 & {- 2} & 1 \\{- 1} & 2 & 0 & {- 2} & 1 \\{- 1} & 2 & 0 & {- 2} & 1 \\{- 1} & 2 & 0 & {- 2} & 1 \\{- 1} & 2 & 0 & {- 2} & 1\end{pmatrix}} + {\frac{1}{144}\begin{pmatrix}4 & 2 & 0 & {- 2} & {- 4} \\{- 2} & {- 1} & 0 & 1 & 2 \\{- 4} & {- 2} & 0 & 2 & 4 \\{- 2} & {- 1} & 0 & 1 & 2 \\4 & 2 & 0 & {- 2} & {- 4}\end{pmatrix}}}$

[0231] The center of each image is divided into an 8×8 array of blocksof size 32×32. The array is chosen to avoid the image borders. Theborders can contain extraneous material, that is, not part of thecervix. In normal use, the physician attempts to keep the cervix in themiddle of the image.

[0232] For each of the blocks the normalized inner product with thecorresponding block in the adjacent motion compensated image iscomputed:$P_{i,j} = \frac{\sum{x\quad \varepsilon \quad B_{i,j}{{I_{2}(x)} \cdot {I_{2}(x)}}}}{\sqrt{\sum{x\quad \varepsilon \quad B_{i,j}{I_{1}^{2}(x)}}} \cdot \sqrt{\sum{x\quad \varepsilon \quad B_{i,j}{I_{2}^{2}(x)}}}}$

[0233] where B_(ij)⊂N² is the domain of definition of block (i,j), andI_(1,2) are the two processed images. The absolute value of P_(i,j) isused as a quality measure.

[0234] The method is used for series of 28 images and the motion isestimated for each of them, except for the first one. Once the motionparameters are determined, the frame is shifted to its computed“correct” location and the above block-based correlation is computedwith the previous shifted image. The result obtained when using theintensity values for each image is plotted with the x-axis correspondingto the different blocks while the y-axis corresponds to the differentimages. For the example presented above, the shifted images matchperfectly and the output is zero intensity everywhere.

[0235] In an alternative embodiment, in which edge information is theonly information used in the matching correlation, the intensity imagesare replaced with sum-of-derivatives images. To date, this embodimenthas provided less favorable motion compensation than the otherembodiment. However, the sum-of derivatives approach appears to providebetter identification of sudden or gross motion.

[0236] While the invention has been particularly shown and describedwith reference to specific preferred embodiments, it should beunderstood by those skilled in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the invention as defined by the appended claims.

What is claimed is:
 1. A method for monitoring effects of chemicalagents on a sample, the method comprising the steps of: dispensing aplurality of chemical agents on a sample, wherein said chemical agentsinteract to alter an optical signal produced by said sample, andmeasuring said altered optical signal.
 2. The method of claim 1, whereinsaid chemical agents interact to produce an additive effect on saidoptical signal.
 3. The method of claim 1, wherein said chemical agentsinteract to reduce an intensity of said optical signal.
 4. The method ofclaim 1, wherein said optical signal is a light spectrum.
 5. The methodof claim 4, wherein said light spectrum is a fluorescent spectrum. 6.The method of claim 1, wherein said optical signal is produced by anendogenous chromophore.
 7. The method of claim 6, wherein saidendogenous chromophore is a flourophore.
 8. The method of claim 1,wherein said chemical agents are selected from the group consisting ofacetic acid, formic acid, propionic acid, butyric acid, Lugol's iodine,Shiller's iodine, methylene blue, toluidine blue, and indigo carmine. 9.The method of claim 1, wherein said plurality of chemical agents aredispensed substantially simultaneously.
 10. The method of claim 1,wherein said chemical agents are dispensed sequentially.
 11. The methodof claim 1, wherein said optical signal is measured over a predeterminedtime.
 12. The method of claim 1, wherein at least one member of saidplurality of chemical agents alters pH of said sample.
 13. The method ofclaim 1, wherein at least one member of said plurality is selected fromthe group consisting of osmotic agents and ionic agents.
 14. A methodfor monitoring effects of chemical agents on a sample, the methodcomprising the steps of: dispensing a chemical agent on a sample, andmeasuring a change in response to said chemical agent in an opticalsignal from an endogenous chromophore in said sample.
 15. The method ofclaim 14, wherein said endogenous chromophore is a flourophore.
 16. Amethod for monitoring effects of a chemical agent on a sample, themethod comprising the steps of: dispensing a chemical agent on a sample,providing an automated triggering signal to initiate a measurementperiod relative to said dispensing step, and measuring a temporalevolution of an optical signal observed from said sample during saidmeasurement period.
 17. The method of claim 16, wherein said triggeringsignal is provided substantially simultaneously with said dispensingstep.
 18. The method of claim 16, wherein said triggering signal isprovided after said dispensing step.
 19. The method of claim 16, whereinsaid measuring step comprises measuring said temporal evolution at atleast one predetermined time relative to said triggering signal.
 20. Themethod of claim 1 or 16, wherein said dispensing step comprisesdispensing said chemical agent or agents as a mist in a predefinedpattern on said tissue.
 21. The method of claim 20, wherein said patternis substantially circular.
 22. The method of claim 20, wherein saidpattern is substantially annular.
 23. The method of claim 20, whereinsaid mist is a controlled volume.
 24. The method of claim 20, whereinsaid dispensing occurs at a controlled rate.
 25. A method for monitoringthe effects of a chemical agent on a sample, the method comprising thesteps of: dispensing a chemical agent on a sample, capturing a pluralityof sequential images of said sample during a measurement period,automatically aligning a subset of said plurality of images to spatiallycorrelate said subset, and measuring a temporal evolution of an opticalsignal from said subset of spatially correlated images.
 26. The methodof claim 25, wherein said aligning step comprises aligning said subsetto compensate for relative motion between said sample and an opticaldevice.
 27. The method of claim 25, wherein said aligning step comprisesaligning said subset to compensate for relative motion between a firstportion of said sample and a second portion of said sample.
 28. Themethod of claim 25, wherein said measuring step is performed atpredetermined times relative to said dispensing step.
 29. The method ofclaim 25, wherein said sample is selected from the group consisting ofcervical tissue, skin, colorectal tissue, and gastric tissue.
 30. Themethod of claim 1, wherein said optical signal is approximated by adecay function.
 31. The method of claim 6 or 14, wherein said endogenousmolecule is selected from the group consisting of NADH, collagen,elastin, flavins, hemoglobin, and porphyrins.
 32. The method of claim 4,wherein said spectrum is produced at least in part by light scatteringproperties of said tissue.